Strongest alignment: Conventional–Investigative (C/I).
Passions include AI, logistics, math, architecture, and electronic music.
Childhood aspiration (architect) aligns with structured, pattern-based thinking.
Ideal long-term activity: counseling students on career choices.
Consistent preference for improving existing systems rather than creating from scratch.
Profile aligns with actuarial-type roles, analytics, systems optimization, finance, or educational/career guidance.
Skilled / hands-on roles aligned with the student's profile. This section will be filled automatically.
Technical and systems-focused roles aligned with the student's profile. This section will be filled automatically.
Professional and analysis-oriented roles aligned with the student's profile. This section will be filled automatically.
Deep-dive into actual schools & training, hiring pathways, job titles, certs, companies/contacts, & what to avoid provided on request.
SOC proxy: TBD — will be mapped to the closest O*NET or adjacent compliance / risk code in the final Greg report.
As organizations deploy AI systems into hiring, finance, healthcare, and government, regulators expect them to prove those systems are safe, fair, and well-controlled. An AI Governance and Compliance Analyst helps design and maintain the frameworks, policies, and evidence that show AI is being used responsibly. The role exists to keep AI programs aligned with laws, internal standards, and stakeholder expectations.
Where traditional compliance focused on financial reporting or privacy, AI governance adds new questions: how models are trained, how data is used, how bias is controlled, and how automated decisions are documented. Instead of occasional audits, AI systems require continual monitoring, model inventories, and change-control. Analysts now work closely with technical teams, not just legal or finance.
AI governance is ultimately about protecting people—customers, employees, and the public—from opaque or harmful algorithmic decisions. Analysts translate legal and ethical expectations into clear rules that data scientists and engineers can follow. They need calm judgment, integrity, and a steady commitment to “doing it right” even when shortcuts are tempting.
Common backgrounds include compliance, risk, data governance, or policy roles, combined with basic literacy in data and AI concepts. Education paths might include business, statistics, public policy, law, or information systems, often with specialized training or certificates in AI governance, responsible AI, or data ethics. Many people grow into this role from traditional compliance or risk departments.
This work happens in quiet, structured office or hybrid environments, often within risk, compliance, or data governance teams. The pace is steady rather than chaotic, with project cycles tied to model releases, audits, and regulatory changes. Travel is limited; most work is computer-based, involving documents, spreadsheets, and structured meetings with stakeholders.
This role matches your need for structured, rule-based work with clear success metrics. You would excel at reviewing AI systems for adherence to standards because it relies on logic, documentation, and measurable criteria. The work is quiet, detail-heavy, and behind-the-scenes—ideal for your introverted, methodical nature. It also supports your interest in AI without requiring fast-paced engineering work.
AI governance roles cluster in major metro areas and tech hubs, especially where financial services, healthcare systems, and large tech companies operate. Remote and hybrid roles are becoming more common as AI programs and compliance teams globalize.
Starting range: ~\$80k–\$110k.
Mid-career range: ~\$120k–\$170k (higher at large financial, tech, or regulated employers).
Broad national estimates based on AI governance, AI policy, and risk/compliance analyst roles; actual pay varies by region, sector, and regulatory exposure.
If you chose to pivot later, experience in AI governance transfers well into broader compliance, operational risk, data governance, or policy roles. The structured way of thinking about controls and documentation is valuable across many parts of large organizations.
Notes on Salary Sources: Approximate ranges extrapolated from current AI governance, AI policy analyst, and risk/compliance roles in large organizations.
SOC proxy: TBD — likely mapped to a hybrid of compliance analyst and content / data governance roles in the final Greg report.
Organizations increasingly rely on large language models and chatbots for customer support, internal knowledge, and content generation. An AI Prompt Compliance Analyst ensures that prompts, templates, and workflows built around these models comply with legal, ethical, and brand guidelines. The role exists to prevent AI systems from generating sensitive, biased, or non-compliant content.
Previously, compliance checks focused on human-written documents, marketing copy, or scripts. With generative AI, prompts themselves become critical control points. Instead of reviewing final outputs only, organizations now design and test guardrails at the prompt and system-instruction level. Analysts bring structure and QA to what might otherwise be ad-hoc experimentation.
This role protects customers and employees from confusing, misleading, or harmful AI responses. Analysts work quietly behind the scenes, reviewing prompts, edge cases, and escalation paths so that frontline teams can trust the tools they’re given. Patience and a calm, systematic approach to testing are more important than extroverted communication.
Backgrounds may include compliance, technical writing, quality assurance, or content review. Training paths often combine a bachelor’s degree (business, communications, information systems, or related field) with hands-on experience using AI tools and prompt design. Short courses or certificates in responsible AI or prompt engineering can help, but most skill-building happens on the job.
Most AI prompt compliance work is done in calm, office or remote settings as part of product, compliance, or AI platform teams. Work is project-based and documentation-heavy, with regular but predictable collaboration. Hours are typically standard business schedules, with peaks when new AI features or regulations roll out.
This position fits your preference for analyzing systems, spotting inconsistencies, and applying structured rules. Evaluating prompts against approved standards is calm, predictable work that requires precision. You would appreciate the lack of ambiguity and the clear definition of what “correct” looks like. The role also leverages your interest in AI while keeping interactions minimal.
Opportunities cluster around tech hubs, financial centers, and large service organizations adopting generative AI at scale. Remote and hybrid roles are increasingly available because prompt libraries and testing tools are cloud-based.
Starting range: ~\$75k–\$105k.
Mid-career range: ~\$110k–\$150k (higher at large tech or regulated employers and for candidates with deep AI experience).
Estimates based on AI policy / governance, specialized compliance, and AI content-focused roles; actual pay depends on sector, location, and AI maturity.
If you later move on, experience here supports transitions into AI governance, content / documentation standards, or broader compliance analyst roles. The discipline of testing and documenting AI behavior is increasingly valuable in many areas.
Notes on Salary Sources: Ranges extrapolated from AI policy analyst, compliance analyst, and AI-adjacent content strategy roles.
SOC proxy: TBD — closest to data quality analyst / data governance technician in the final Greg report.
AI systems depend on clean, consistent data. If the underlying datasets are wrong, duplicated, or mislabeled, models will produce unreliable results. An AI Data Consistency Verifier focuses on checking and enforcing data quality rules, especially for datasets feeding machine learning and analytics. The role exists to reduce silent errors that would otherwise slip through into automated decisions.
Traditional data entry or reporting roles often checked data only at the surface level. In AI programs, quality checks must be systematic, repeatable, and tailored to model needs. Instead of ad-hoc fixes, organizations now build structured validation steps into data pipelines. This creates demand for people who specialize in patterns, edge cases, and consistency rules.
Although much of the work is technical, the impact is human: fewer billing mistakes, more accurate eligibility decisions, and more trustworthy analytics. Data consistency verifiers quietly protect customers and coworkers by catching errors before they propagate. Their persistence and patience keep teams from making big decisions on shaky numbers.
Many people enter through data analyst, reporting, or operations roles. Helpful education paths include statistics, information systems, business analytics, or related fields, often combined with short courses in SQL, Python, or data tools. On-the-job training focuses on the organization’s specific data models and validation rules.
The work is typically quiet, screen-based, and highly structured, often within analytics, data governance, or operations teams. Schedules are usually standard business hours with predictable workloads tied to data refresh cycles or project timelines. Travel is rare, and most collaboration happens through tickets, chats, or scheduled meetings.
This role aligns with your strength in noticing patterns and ensuring accuracy across datasets. It provides structured, repetitive analysis that suits your desire for predictability and clear procedures. The work is highly independent and quiet. You would enjoy the satisfaction of ensuring data quality with clear, measurable outcomes.
Data quality roles appear anywhere large datasets are used—especially in metro areas with healthcare systems, financial institutions, tech companies, and government agencies. Remote and hybrid roles are increasingly common for data-centric work.
Starting range: ~\$65k–\$90k.
Mid-career range: ~\$95k–\$130k (higher for roles embedded in high-stakes financial or healthcare data programs).
Estimates drawn from current data quality analyst and data analyst compensation; actual pay varies with sector, tools, and responsibility level.
From here, you could move into broader data analyst roles, data governance positions, or specialized quality roles in finance or healthcare. The habit of carefully validating information is valuable wherever numbers drive decisions.
Notes on Salary Sources: Based on national data quality analyst and data analyst salary benchmarks.
SOC proxy: TBD — closely aligned with IT risk and controls analyst roles in the final Greg report.
As AI systems move into core business processes, boards and regulators need clear evidence that risks are identified, measured, and controlled. An AI Risk and Controls Coordinator helps map AI-related risks—such as bias, model drift, security, and misuse—to specific controls and monitoring activities. The role exists to ensure AI programs don’t become unmanaged “black boxes.”
Traditional IT risk work focused on servers, networks, and access rights. AI introduces new risk types: training data quality, explainability, and model lifecycle management. Coordinators now work with data scientists and product owners to embed risk controls into model development and deployment pipelines, not just infrastructure.
The coordinator protects customers, employees, and the organization’s reputation by quietly insisting that safeguards are in place. They’re the structured voice asking, “What could go wrong, and how will we catch it?” This requires calm persistence, logical thinking, and comfort enforcing rules even when others want to move fast.
Many people come from IT risk, internal audit, or operational risk roles and add AI literacy over time. Recommended education includes business, information systems, or risk management degrees, plus certifications in IT risk, cybersecurity, or AI governance. Short courses in machine learning fundamentals help in understanding model-specific risks.
The environment is typically corporate, predictable, and procedure driven, often within second-line risk or compliance teams. Work is mostly indoors and screen-based, with scheduled meetings rather than constant interruptions. Travel is limited, and schedules tend to follow standard business hours, especially outside of audit season.
You would thrive in this role because it centers on clear rules, logic, and methodical evaluation. The work involves assessing risks using structured frameworks, which matches your analytical style. It is behind-the-scenes and focused on accuracy rather than persuasion. The predictability and clear metrics would keep you grounded and engaged.
Roles are concentrated in financial centers and regions with large employers subject to heavy regulation. Remote and hybrid opportunities are expanding as risk teams support global AI initiatives across multiple locations.
Starting range: ~\$75k–\$100k.
Mid-career range: ~\$110k–\$150k (upper end for complex portfolios or regulated industries).
Based on IT risk and controls analyst compensation, adjusted for AI-focused responsibilities; pay varies by sector, city, and regulatory intensity.
From here, you could move into broader risk management, internal audit, governance roles, or back toward more general compliance or cybersecurity positions. The structured understanding of risks and controls is portable across many industries.
Notes on Salary Sources: Derived from IT risk and controls analyst, GRC analyst, and related roles in financial and enterprise settings.
SOC proxy: TBD — conceptually closest to machine learning engineer / data scientist roles with a focus on fairness and ethics.
AI systems can unintentionally treat groups of people differently based on race, gender, age, or other attributes. An Algorithmic Bias Monitoring Specialist designs tests and monitoring to detect these patterns and push for corrective action. The role exists to keep AI-driven decisions aligned with fairness standards, regulations, and organizational values.
Historically, fairness concerns were handled via policy statements or manual reviews. With AI, bias can emerge from complex data and model interactions that are hard to see without deliberate testing. This role formalizes bias detection as part of model development and ongoing monitoring, using metrics, dashboards, and structured experiments.
The specialist stands at the intersection of data, ethics, and human impact. Their work protects individuals from unfair treatment in areas like lending, hiring, healthcare, and education. While they may not be in the spotlight, they represent the interests of groups who might otherwise be harmed by invisible model behavior.
Backgrounds often include statistics, data science, economics, or social science combined with quantitative methods. Additional study in ethics, law, or public policy is helpful. Many practitioners build experience as data scientists or analysts before specializing in fairness and responsible AI, supported by short courses and internal training.
Work is primarily analytical and computer-based, often within data science, research, or responsible AI teams. The environment is typically quiet and project-oriented, with collaborative meetings but extended periods of independent analysis. Hours are generally standard business schedules, with occasional surges around audits or major deployments.
This job fits your analytical mindset and preference for objective, data-driven evaluation. Monitoring models for bias involves clear criteria, measurable deviations, and structured review cycles. You would appreciate the precision required to detect patterns and inconsistencies. The quiet, independent nature of the work matches your social preferences.
Bias and fairness roles are most common in major tech hubs, financial centers, and research-heavy regions, but remote roles are growing as organizations build distributed responsible AI teams.
Starting range: ~\$95k–\$130k.
Mid-career range: ~\$140k–\$190k (with higher ranges at top tech, finance, or specialized AI firms).
Ranges extrapolated from machine learning engineer, data scientist, and responsible AI specialist roles; compensation can be significantly higher at elite employers.
Experience here supports transitions into broader data science, model risk management, AI governance, or policy roles. The combination of statistical rigor and ethical awareness is increasingly in demand across industries.
Notes on Salary Sources: Estimated from current ML engineer, data scientist, and responsible AI compensation data in tech and finance.
SOC proxy: TBD — closely aligned with model risk analyst and IT audit roles in the final Greg report.
As AI models move into critical decisions, organizations must be able to prove how those models were built, changed, and used over time. An AI Model Audit and Traceability Analyst creates the documentation, logs, and evidence trails that show who did what, when, and under which controls. The role exists so that regulators, customers, and internal leaders can trust that AI is not a black box.
Traditional IT audit focused on servers, applications, user access, and change management at the system level. With AI, the focus expands to training data, model versions, hyperparameters, deployments, and monitoring. Instead of just checking system configurations, auditors now need structured inventories of models, reproducible workflows, and end-to-end traceability from data to decision.
Behind every AI system are people who may be affected by its decisions—customers approved or denied, patients prioritized or delayed, candidates hired or filtered out. The analyst protects those individuals by ensuring the organization can explain and, if needed, correct its AI behavior. Calm persistence, integrity, and comfort enforcing structure are more important than charisma.
Most people grow into this role from IT audit, internal audit, data analytics, or model risk teams. Helpful education paths include accounting, information systems, statistics, data analytics, or operations research, often paired with certifications in audit, risk, or data. Short courses in machine learning fundamentals and MLOps tools support the technical side of model traceability.
The work is quiet, documentation-heavy, and largely screen-based. AI model audit typically sits within risk, internal audit, or data governance teams in mid- to large-sized organizations. Schedules follow standard business hours with predictable peaks around audits or regulatory deadlines. Travel is limited; much of the work is done via meetings, repositories, and structured reports.
This role aligns with your desire for order, structure, and detailed system evaluation. Auditing models requires following step-by-step procedures and verifying clear documentation trails, which play to your strengths. You would enjoy the logical rigor and independence of the work. The predictable process and absence of chaos make it an excellent fit for your temperament and work-life preferences.
Model audit and traceability roles are concentrated in financial centers, tech hubs, and large enterprise headquarters, but remote and hybrid options are expanding as AI governance becomes more distributed. Major banks, insurers, and global corporations are likely early adopters.
Starting range: ~\$90k–\$130k.
Mid-career range: ~\$135k–\$185k (higher in large financial institutions and specialized audit / consulting firms).
Estimates based on model risk analyst, IT audit, and AI assurance roles; actual compensation varies by sector, geography, and regulatory exposure.
Experience in AI model audit transfers well to internal audit, broader technology risk, model risk management, and governance roles. The habits of careful documentation, evidence gathering, and structured evaluation remain valuable even if AI regulations shift over time.
Notes on Salary Sources: Approximate ranges extrapolated from model risk, IT audit, and AI governance roles in large organizations.
SOC proxy: TBD — aligned with technical documentation, knowledge management, and governance lead roles in the final Greg report.
AI systems produce complex behavior that can be misunderstood or misused if documentation is weak. An AI Documentation Standards Lead defines the templates, guidelines, and expectations for how AI projects are documented across the organization. The role exists to ensure consistent, clear records that make AI systems understandable, reviewable, and maintainable over time.
Traditional documentation roles focused on user manuals or developer guides for software. With AI, documentation must also capture data lineage, model assumptions, training processes, monitoring plans, and ethical considerations. Instead of one-off documents, organizations need standardized documentation frameworks that every AI team follows.
Clear documentation protects both internal teams and end users from confusion and unintended consequences. The standards lead acts as a quiet force for clarity, turning messy, ad-hoc notes into structured knowledge. They reduce stress for engineers, auditors, and decision-makers by making it easy to understand what a system does and how it was built.
People often move into this role from technical writing, knowledge management, QA, or governance positions. Helpful education paths include information systems, technical communication, library / information science, or related fields. Short courses in AI fundamentals, data governance, and compliance help connect documentation practices to AI-specific needs.
This role is mostly office or remote-based, with extended time spent reading and editing documents. Work is structured and deadline-driven but not chaotic, with planned review cycles and update schedules. Collaboration happens through scheduled meetings and document reviews rather than constant real-time interaction.
This role matches your need for precision and structured workflows. You would excel at enforcing documentation standards and ensuring consistency across teams. The work is predictable, quiet, and highly organized. It allows you to create clarity through detailed systems without requiring heavy public interaction or sales-type communication.
AI documentation and standards roles are most common in organizations with mature AI programs—often in tech hubs, financial centers, and large enterprise headquarters. Remote opportunities are growing, since documentation standards can be managed across distributed teams.
Starting range: ~\$80k–\$115k.
Mid-career range: ~\$115k–\$150k (higher for large, regulated organizations or leadership roles).
Estimates based on senior technical writer, documentation lead, and knowledge-management roles with AI / governance focus.
If you later pivot, this experience supports moves into broader knowledge management, compliance documentation, or AI governance coordination roles. The underlying strength—creating order and clarity from complexity—remains valuable across many departments.
Notes on Salary Sources: Based on technical documentation, information governance, and AI program roles with leadership responsibility.
SOC proxy: TBD — similar to senior compliance analyst and policy / procedure designer roles in the final Greg report.
Many organizations have thick policy binders that nobody reads. A Compliance Playbook Designer turns those rules into clear, step-by-step guides that real people can follow in daily work—especially when using complex tools like AI. The role exists to translate legal and regulatory expectations into practical workflows and checklists.
Traditional compliance often produced long narrative policies and occasional training sessions. Modern organizations need operational playbooks: concise actions, decision trees, and examples that show exactly what to do. Instead of only writing policies, this role designs structured, re-usable playbooks that plug into tools, training, and audits.
At its core, this work helps ordinary employees feel less overwhelmed by rules. A good playbook designer quietly reduces anxiety by saying, “Here’s the exact path to follow.” They protect customers and the organization by making it easier to do the right thing than the wrong thing, even under time pressure.
People often come from compliance, risk, operations, or training backgrounds. Useful education paths include business, law-related studies, public policy, or information systems. Many practitioners add specialized training in process design, instructional design, or AI governance to support more complex workflows.
The work is mostly quiet and project-based, often within compliance, operations, or learning & development teams. Days are spent reading policies, designing artifacts, and reviewing drafts with subject-matter experts. Schedules are predictable, with clear project milestones instead of constant emergencies.
You would thrive in this role because it involves creating structured, step-by-step guidelines. The work is analytical, methodical, and behind-the-scenes. You would enjoy turning complex rules into clear, organized procedures. It also supports your desire for predictable, measurable outcomes and your preference for written, structured communication.
Compliance playbook roles appear anywhere regulations are complex and enforcement is serious—financial centers, healthcare hubs, and large corporate headquarters. Remote consulting and documentation roles are increasingly common.
Starting range: ~\$80k–\$115k.
Mid-career range: ~\$115k–\$150k (higher in heavily regulated industries or for senior-level designers).
Based on senior compliance analyst, policy designer, and regulatory operations roles, adjusted for playbook-design emphasis.
From here, you could move into broader compliance leadership, risk operations, or training design roles. The ability to operationalize rules into practical workflows is widely valuable and portable.
Notes on Salary Sources: Ranges extrapolated from compliance analyst, regulatory operations, and process design roles.
SOC proxy: TBD — closest to operations analyst and business process auditor roles in the final Greg report.
As organizations digitize more of their operations—tickets, approvals, customer journeys—they need assurance that these workflows are efficient, compliant, and logically designed. A Digital Workflow Auditor reviews end-to-end processes to find bottlenecks, errors, and misalignments. The role exists to make digital operations smoother, safer, and easier to manage.
Traditional auditors often looked at paper processes, localized procedures, or sample transactions. Digital workflows introduce complex, cross-system paths that can only be understood by tracing data through tools, APIs, and dashboards. Instead of isolated checks, workflow auditors examine the full chain of steps, hand-offs, and decision rules embedded in software.
Behind every workflow are people trying to get their work done without friction. A good workflow auditor quietly improves their day by removing unnecessary steps, clarifying responsibilities, and reducing rework. The role helps employees feel less frustrated by systems and helps customers experience smoother service.
People often enter from operations, business analysis, internal audit, or process-improvement roles. Common education paths include business, information systems, industrial engineering, or operations management. Short courses in process mapping, Lean / Six Sigma, or workflow tools are useful complements.
The work is quiet and analysis-heavy, combining time with process maps, logs, and stakeholder interviews. It typically follows project timelines with defined phases rather than constant emergencies. Hybrid and remote arrangements are common, and travel is modest unless tied to multi-site implementations.
This career matches your love of improving existing systems through structured evaluation. You would excel at reviewing workflows for consistency, efficiency, and compliance with established rules. The work allows quiet, focused analysis without social pressure. It also gives you measurable outcomes and clear before-and-after comparisons, which you prefer.
Digital workflow auditors are needed anywhere large organizations rely heavily on software to run operations—metro areas with hospitals, universities, corporate headquarters, and government agencies are especially likely to host these roles. Remote roles are becoming more common as tools and logs move to the cloud.
Starting range: ~\$75k–\$105k.
Mid-career range: ~\$105k–\$140k (higher with consulting, specialized domains, or leadership responsibilities).
Ranges based on business process analyst, internal auditor, and operations improvement roles with digital focus.
From this role, you could move into operations management, process-improvement leadership, internal audit, or digital transformation consulting. The ability to analyze and improve workflows is relevant in almost every large organization.
Notes on Salary Sources: Estimated from operations analyst, internal audit, and process-improvement compensation in mid- to large-sized organizations.
SOC proxy: TBD — related to reliability engineer, business continuity planner, and systems architect roles in the final Greg report.
Modern organizations depend on complex combinations of software, infrastructure, and vendors. When a critical system fails, the impact can be severe. A Systems Redundancy Strategist designs backup paths, failover strategies, and contingency plans so that essential services continue even when something breaks. The role exists to reduce downtime and protect both revenue and trust.
Older continuity planning often focused on data backups and disaster recovery sites. Today, with cloud services, APIs, and AI in the mix, redundancy planning must consider dependencies across many layers: applications, data pipelines, identity systems, and external providers. Strategists take a more architectural view, designing resilient patterns instead of single backup scripts.
While the work is technical, the impact is highly human: employees can keep serving customers, hospitals continue critical care, and students still access learning systems even when something goes wrong. The strategist is the calm planner who anticipates failure modes and quietly sets up protections so frontline teams never experience major disruptions.
People often come from systems administration, network engineering, reliability engineering, or IT architecture roles. Education paths include computer science, information systems, or engineering disciplines. Certifications in cloud platforms, reliability, or continuity planning support credibility, but much learning comes from understanding real incident patterns.
Work is largely office or hybrid, combining design time with scheduled tests and reviews. While there may be occasional after-hours involvement during major incidents, the core job is proactive planning, not constant firefighting. The environment suits someone who prefers deliberate, structured work over chaotic, real-time troubleshooting.
This role leverages your strength in planning, logical thinking, and risk reduction. Designing backup systems requires structure, predictability, and attention to detail—perfect for your problem-solving style. You would appreciate the independence and clarity of evaluating failure points. The work is quiet and methodical, matching your preferred environment and desire for stable, behind-the-scenes impact.
Systems redundancy roles are common in major metros with data centers, large enterprises, and cloud-reliant organizations. However, hybrid and remote roles are increasingly feasible because much of the planning work is tool- and documentation-based.
Starting range: ~\$85k–\$120k.
Mid-career range: ~\$120k–\$160k (higher in sectors where downtime is extremely costly, such as finance or healthcare).
Estimates based on reliability engineer, continuity planner, and systems architect compensation, adjusted for redundancy focus.
From here, you could move into reliability engineering leadership, enterprise architecture, risk and continuity management, or specialized resilience consulting. The core skill—designing systems that keep working under stress—remains valuable even as specific technologies evolve.
Notes on Salary Sources: Derived from reliability engineering, continuity planning, and infrastructure architecture salary benchmarks.
SOC proxy: TBD — similar to business process designer / operations analyst roles in the final Greg report.
Organizations often have scattered procedures, outdated manuals, and tribal knowledge instead of clear, unified ways of operating. An Operational Playbook Systems Designer creates structured playbooks that show exactly how work should flow across teams and systems, often including AI-enabled steps. The role exists to turn messy, informal processes into consistent, repeatable operations.
Traditional procedure writing was frequently ad-hoc and text-heavy, producing documents that nobody read. Today, operations need visual, tool-aware, and measurement-focused playbooks that integrate tickets, dashboards, and AI tools. Instead of static manuals, the designer builds living operational systems that can be updated and improved over time.
This role quietly reduces confusion and stress for frontline employees by giving them clear, step-by-step guides. It helps managers feel more confident that work is being done correctly and consistently. The designer doesn’t sit in the spotlight; they support others by bringing order, clarity, and predictability to everyday tasks.
Many people come from operations, business analysis, quality assurance, or process-improvement backgrounds. Common education paths include business, operations management, industrial engineering, or information systems. Additional training in process mapping, service design, or AI-enabled tools (like workflow automation platforms) is highly useful.
The work is quiet, project-based, and primarily screen-focused, with scheduled meetings to gather information and review drafts. It usually follows standard business hours and offers a predictable routine, especially once major transformation projects are underway. Travel is limited unless supporting multiple sites in person.
This job fits your preference for improving existing systems through clear structure and documentation. You would excel at designing operational guides that follow logical, linear sequences. The work is quiet and highly independent, allowing you to focus deeply without constant interruptions. It gives you the satisfaction of bringing order and clarity to complex processes in a way that aligns well with your profile.
Operational playbook roles appear wherever organizations are formalizing processes—especially in metro areas with corporate headquarters, hospitals, universities, and government agencies. Remote and hybrid roles are increasingly common as documentation and collaboration tools move online.
Starting range: ~$80k–$115k.
Mid-career range: ~$115k–$150k (higher in large enterprises or consulting environments).
Estimates based on business process designer, operations analyst, and service design roles with playbook responsibility.
From here, you could move into operations management, process-improvement leadership, service design, or digital transformation roles. The core capability—designing clear, repeatable systems—is broadly useful and transferable.
Notes on Salary Sources: Approximate ranges extrapolated from process design, operations analysis, and transformation roles.
SOC proxy: TBD — similar to operations center analyst / systems monitoring roles in the final Greg report.
Many organizations rely on control centers to monitor systems, services, or logistics in real time. A Quiet Operations Control Center Analyst focuses on stable, well-structured environments where the goal is to catch issues early and keep everything running smoothly—not to firefight chaos all day. The role exists to provide steady, reliable oversight of critical operations.
Traditional operations centers can be noisy, high-pressure environments with constant alerts and urgent calls. Quiet control centers are intentionally designed to reduce noise and emphasize thoughtful monitoring, pattern recognition, and preventive action. The analyst relies more on structured dashboards and procedures than rapid, ad-hoc reactions.
This role supports the people who depend on those systems—employees, customers, or students—by making problems invisible to them. When the analyst does their job well, others experience smooth, uninterrupted service. It suits someone who wants to help from behind the scenes without being the face of the operation.
Backgrounds often include operations, IT support, logistics, facilities monitoring, or network operations. Education may range from an associate degree to a bachelor’s in information systems, operations management, or a related field. On-the-job training focuses on the specific monitoring tools, dashboards, and escalation paths used in that environment.
The environment is usually quiet and controlled by design, with screens, dashboards, and headsets rather than heavy foot traffic. Some roles may involve shift work, but the emphasis is on stability rather than crisis management. For someone who values predictable routines, this can be a good fit if the schedule and hours are chosen wisely.
You would thrive in this role because it provides a quiet, stable environment with clear procedures. Monitoring operations requires precision and attention to detail—two of your strongest traits. The work is predictable and rules-based, with well-defined success metrics. It keeps you behind-the-scenes, aligning well with your social preferences and desire for low-conflict settings.
Operations control centers exist in metro areas with campuses, logistics hubs, data centers, or large facilities. Some monitoring roles can be performed remotely as tools move to the cloud, though physical presence may still be needed in certain environments.
Starting range: ~$70k–$100k.
Mid-career range: ~$100k–$135k (higher when tied to critical infrastructure or specialized systems).
Estimates based on operations center analyst, NOC analyst, and facilities monitoring roles, adjusted for quieter, more process-driven settings.
Experience here can lead to roles in operations management, reliability / continuity planning, facilities coordination, or systems monitoring design. The discipline of steady, structured oversight is valuable in many operational domains.
Notes on Salary Sources: Approximate ranges extrapolated from control center, NOC, and operations analyst positions.
SOC proxy: TBD — similar to logistics analyst / facilities planner roles in the final Greg report.
Even “quiet” facilities—libraries, campuses, labs, offices, specialized storage—need careful planning for movement of people, materials, and services. A Quiet Facilities Logistics Planner designs how supplies, equipment, and activities flow through low-noise, low-chaos environments. The role exists to keep everything running smoothly without disrupting the calm atmosphere.
Traditional logistics planning often focuses on high-volume warehouses, trucking, or noisy industrial spaces. Quiet environments introduce different constraints: minimizing distractions, preserving safety and accessibility, and coordinating specialized deliveries. The work requires more emphasis on subtle flows and schedule design than on maximum throughput.
This role supports people who need calm, focused spaces—students, researchers, professionals, patients—by ensuring that logistical needs are handled in the background. When done well, occupants barely notice the planning effort; they simply experience a well-functioning, low-distraction environment.
People may come from logistics coordination, facilities management, operations analysis, or planning roles. Education paths include supply chain management, operations management, industrial engineering, or related fields. Additional training in facilities planning, safety, or specialized environments (like labs or archives) can be valuable.
The environment is typically quiet and structured, with time split between planning at a desk and walking the facility to observe real-world conditions. The role follows standard business hours most of the time, with occasional adjustments around special events or projects. It suits someone who appreciates calm spaces but enjoys thinking about how things move behind the scenes.
This role is almost tailor-made for your desire for quiet, calm, structured spaces. Planning logistics for low-noise or low-traffic environments allows you to apply your interest in logistics without chaotic conditions. You would enjoy the predictable routines and the analytical nature of planning. It also aligns with your preference for behind-the-scenes work and your strength in structured problem-solving.
Quiet facilities logistics roles are concentrated around universities, hospitals, research parks, and government complexes—especially in metro areas that host large campuses. Some planning work can be done remotely, but on-site familiarity with the facility is usually important.
Starting range: ~$70k–$100k.
Mid-career range: ~$100k–$135k (higher in large healthcare or research institutions).
Estimates based on logistics analyst, facilities planner, and campus operations roles, adjusted for quiet-environment planning.
From this role, you could move into broader facilities management, campus operations planning, or logistics analysis. The expertise in calm, structured environments can also translate to specialized consulting or design work.
Notes on Salary Sources: Approximate ranges extrapolated from logistics and facilities planning positions.
SOC proxy: TBD — similar to operations research / process design roles with AI tooling in the final Greg report.
AI tools can now simulate workflows, forecast workloads, and recommend process changes, but organizations still need people who design the underlying operational blueprints. An AI-Assisted Operations Blueprint Designer uses data and AI to design how work should be structured, scheduled, and coordinated. The role exists to combine human judgment with machine insights to build better operations.
Traditional operations research relied on models that were built and updated manually. Today, AI and advanced analytics can explore many more scenarios, but only if someone defines the goals, constraints, and structures clearly. The blueprint designer focuses on representing operations in a way that AI tools can analyze, then translates the results back into human-friendly designs.
This role helps people experience smoother, more predictable workdays by reducing bottlenecks and confusion. The designer doesn’t just optimize numbers; they consider what it feels like to work within a system. They are a behind-the-scenes architect of daily experience, using structured thinking rather than charisma to make a difference.
Common backgrounds include industrial engineering, operations research, business analytics, or process design. Education often involves math, statistics, and modeling courses, plus exposure to optimization or simulation tools. Short courses in AI-assisted planning tools and workflow platforms enhance effectiveness in this emerging niche.
The work is mostly analytical and desk-based, with occasional workshops or walkthroughs to understand real operations. Projects follow phased timelines, offering focus periods rather than constant interruption. Standard business hours are typical, with limited travel depending on how many locations are involved.
This job matches your desire to improve existing systems through structured analysis. Creating operational blueprints using AI tools allows for clear procedures, checklists, and logical planning. You would excel at translating complex processes into organized, repeatable workflows. The work is quiet, analytical, and highly methodical, aligning closely with your cognitive style and work-life expectations.
AI-assisted operations design roles are likely to appear in metro areas with large service organizations, consulting firms, and tech-enabled operations teams. Remote work is increasingly possible when process data and tools are accessible online.
Starting range: ~$85k–$120k.
Mid-career range: ~$120k–$165k (higher in consulting, large enterprises, or highly optimized operations).
Estimates based on operations research analyst, process designer, and analytics-focused roles that leverage AI tools.
From this role, you could move into operations research, analytics leadership, transformation consulting, or internal operations strategy. The underlying skills in modeling and structured design remain valuable even as specific AI tools evolve.
Notes on Salary Sources: Approximate ranges extrapolated from operations research and analytics roles in service-heavy organizations.
SOC proxy: TBD — closely aligned with operations research analyst / logistics modeler roles in the final Greg report.
Organizations face many “what if” questions about logistics: demand spikes, route changes, supply delays, or new facility locations. A Logistics Scenario Modeling Specialist builds and runs models to test different scenarios before major decisions are made. The role exists to reduce risk and improve decisions about how goods and services move.
Traditional logistics planning relied on spreadsheets, experience, and static assumptions. Today, advanced analytics and AI can explore many scenarios quickly, but they still require carefully designed models and thoughtful interpretation. The specialist focuses on building structured simulations and using data to compare options before committing resources.
Behind the models are real-world consequences: fewer stockouts, better on-time performance, and smoother experiences for customers and staff. The specialist’s quiet work helps people avoid crises and surprises. They provide calm, logic-based guidance in areas where decisions can otherwise feel uncertain or political.
Common backgrounds include industrial engineering, supply chain management, operations research, or applied mathematics. Education often includes coursework in statistics, optimization, simulation, and modeling tools. Short courses in logistics software, geographic information systems (GIS), or AI planning tools can further strengthen capabilities.
The role is heavily analytical and desk-based, with occasional visits to warehouses, hubs, or facilities to understand operations. It follows project timelines, offering periods of deep focus on modeling and reporting. Standard business hours are typical, with limited travel depending on how many sites must be studied.
This job combines your interest in logistics with your analytical mindset. Scenario modeling requires structured thinking, planning, and evaluating measurable outcomes. You would enjoy running simulations and improving system efficiency. The work is calm, logical, and independent, matching your preference for quiet, behind-the-scenes problem-solving.
Logistics modeling roles cluster around major logistics hubs, port cities, distribution centers, and large metropolitan areas with supply-chain intensive industries. Some modeling work can be done remotely for organizations that centralize analytics teams.
Starting range: ~$90k–$125k.
Mid-career range: ~$130k–$175k (higher in large global logistics firms or specialized consulting).
Estimates based on operations research analyst, logistics analyst, and supply-chain modeling roles.
From here, you could move into broader supply-chain analytics, operations research, network design, or logistics strategy roles. The modeling and scenario-planning skills are valuable across many industries that move goods or services.
Notes on Salary Sources: Approximate ranges extrapolated from logistics analytics and operations research positions.
SOC proxy: TBD — similar to operations research analyst / scheduling planner roles in the final Greg report.
Schools, hospitals, transportation systems, and large organizations all struggle with complex schedules: rooms, staff, equipment, and services that must align without conflict. An AI-Assisted Scheduling and Timetabling Architect designs the rules, constraints, and structures that AI tools use to generate workable schedules. The role exists to turn messy scheduling challenges into stable, predictable timetables.
Traditional scheduling was often done manually in spreadsheets or with basic software, relying heavily on one person’s memory and experience. AI-enabled schedulers can explore many more options, but only if someone defines clear rules, priorities, and constraints. The architect focuses on representing real-world needs in a structured way that AI can handle, then turning results into practical schedules people can follow.
Good schedules protect people from burnout, conflicts, and last-minute chaos. The architect’s work quietly supports teachers, nurses, staff, and students by giving them predictable, fair, and efficient timetables. This role suits someone who wants to make life more orderly for others without being in the spotlight.
Many come from operations research, industrial engineering, educational administration, or scheduling and planning roles. Education often includes math, statistics, optimization, or operations management. Short courses in AI planning tools, constraint solvers, or timetabling software are helpful, but much of the work relies on structured, logical thinking.
The work is mainly analytical and desk-based, with periodic meetings to collect requirements or present schedule options. It follows project cycles—especially around term starts, staffing changes, or new service launches. Hours are generally standard business schedules, and the environment is well-suited to someone who prefers quiet, structured problem-solving.
This role aligns with your strengths in structure, predictability, and planning ahead. Designing schedules requires logical sequencing and attention to detail—traits you excel in. The work is quiet, methodical, and largely independent. You would enjoy producing orderly solutions to complex constraints, with clear, measurable outcomes and minimal day-to-day chaos.
Scheduling and timetabling architect roles appear wherever there are large, complex schedules—especially in education, healthcare, and transportation. Remote and hybrid work is increasingly possible when scheduling tools and data are online, though some roles require close coordination with on-site staff.
Starting range: ~$80k–$115k.
Mid-career range: ~$115k–$155k (higher in large systems or highly optimized operations).
Estimates based on operations research, workforce management, and advanced scheduling roles that incorporate AI tools.
From here, you could move into broader operations research, workforce planning, educational operations, or logistics optimization roles. The underlying skills in structured constraint modeling and scheduling remain valuable in many industries.
Notes on Salary Sources: Approximate ranges extrapolated from scheduling, workforce planning, and operations research positions.
SOC proxy: TBD — aligned with data quality analyst / data governance roles in the final Greg report.
AI and analytics are only as reliable as the data they use. A Data Quality and Standards Steward defines and enforces the rules that keep data accurate, consistent, and usable across systems. The role exists to prevent silent errors, conflicting records, and broken reports from undermining decisions.
In the past, data quality was often handled informally by individual teams fixing issues as they noticed them. Today, organizations recognize the need for central standards: data definitions, validation rules, and stewardship responsibilities. Instead of ad-hoc cleanups, the steward helps build durable, organization-wide practices.
High-quality data means fewer billing mistakes, better student records, clearer performance reports, and more trustworthy AI outputs. The steward protects both customers and colleagues by making sure the numbers they rely on are solid. It’s a behind-the-scenes role that quietly supports everyone who touches data.
People often come from data analyst, reporting, or operations roles. Education paths include information systems, business analytics, statistics, or related fields. Short courses in data governance, data quality tools, and basic database or SQL skills are helpful to perform this role effectively.
The work is quiet and computer-based, often embedded in analytics, IT, or data governance teams. It follows predictable cycles tied to data refreshes, system changes, and projects. Travel is rare, and most collaboration happens through meetings, tickets, and shared documentation.
This role fits your strong preference for accuracy, precision, and measurable outcomes. You would enjoy enforcing data standards and ensuring consistency across systems. The work is predictable and deeply structured, aligning with your cognitive style. It also keeps you behind-the-scenes with minimal interpersonal demands and clear success metrics.
Data quality and stewardship roles appear wherever organizations invest in centralized data platforms—especially in larger metro areas with hospitals, universities, financial institutions, and corporate headquarters. Remote roles are increasingly common as data tools move to the cloud.
Starting range: ~$70k–$100k.
Mid-career range: ~$100k–$135k (higher where data is highly regulated or safety-critical).
Estimates based on data quality analyst, data steward, and data governance specialist roles.
From here, you could move into broader data governance, analytics, or information management roles. The habits of careful validation, documentation, and structured thinking about data transfer well to many positions.
Notes on Salary Sources: Approximate ranges extrapolated from data quality and data governance positions.
SOC proxy: TBD — similar to information architect / knowledge management roles in the final Greg report.
As organizations accumulate thousands of articles, documents, and FAQs, people struggle to find what they need. A Knowledge Base Taxonomy Architect designs the categories, tags, and structures that organize information in a logical way. The role exists to make knowledge bases easier to search, navigate, and maintain, including those used by AI assistants.
Traditional documentation often grew organically, with inconsistent categories and duplicated content. Today, better search and AI tools still need well-structured information underneath. Instead of treating taxonomy as an afterthought, this role makes it a deliberate, managed discipline, similar to library science but embedded in digital systems.
When taxonomy is done well, employees and customers spend less time frustrated and more time actually solving their problems. The architect supports everyone who relies on documentation by making it intuitive to find. It’s a thoughtful, behind-the-scenes role that rewards patience and a love of order.
Backgrounds often include library and information science, technical communication, UX/content design, or knowledge management. Education may involve taxonomy, classification, ontology, or information architecture courses. Experience with content management systems and search tools is particularly valuable.
The work is quiet and highly focused, typically within knowledge management, documentation, or UX/content teams. It follows project cycles with steady refinement rather than constant urgent crises. Remote and hybrid work are common, as most tasks involve digital tools and collaboration.
This role matches your love of organization, categorization, and logical systems. Building taxonomies requires clear structure, predictable rules, and meticulous planning. You would enjoy the independence and quiet focus of the work. It also lets you improve existing information systems, which is a core preference highlighted in your profile.
Taxonomy and information architecture roles are most common in tech hubs, large service organizations, and corporate headquarters with extensive knowledge libraries. Many positions are remote-friendly due to the digital nature of the work.
Starting range: ~$85k–$120k.
Mid-career range: ~$120k–$160k (higher for senior architects in large enterprises or specialized consultancies).
Estimates based on information architect, taxonomy specialist, and knowledge management roles.
From here, you could move into broader information architecture, UX content strategy, or knowledge management leadership. The core skill—creating logical structure for information—remains essential even as tools change.
Notes on Salary Sources: Approximate ranges extrapolated from information architecture and knowledge management positions.
SOC proxy: TBD — similar to policy documentation / information management roles in the final Greg report.
Organizations generate large volumes of policies, procedures, and guidelines that need to be findable, current, and consistent. A Digital Policy and Procedures Librarian manages the structure, access, and lifecycle of these documents in digital repositories. The role exists to ensure that employees can reliably find “the one right version” of the rules they must follow.
In the past, policies might have lived in binders, scattered files, or shared drives with poor organization. Digital policy libraries demand more deliberate curation: metadata, access rules, version control, and retirement of outdated content. The librarian applies principles of library science and records management to modern digital systems.
When people can’t find clear, current procedures, they feel confused and anxious—and mistakes become more likely. This role quietly supports employees by making it easy to locate trusted guidance. It suits someone who prefers to help others by organizing information rather than by being on the front line.
Backgrounds may include library and information science, records management, compliance documentation, or knowledge management. Education paths often involve information science, archives, or related areas, sometimes combined with organizational policy or compliance coursework.
The environment is quiet and structured, centered around digital systems and documentation workflows. Work follows predictable schedules tied to review cycles and policy updates, with minimal crisis-driven urgency. Remote and hybrid arrangements are common, since most tasks are online.
This role fits your desire for order, documentation, and clear guidelines. You would excel at maintaining organized repositories of procedures because it requires precision and accuracy. The work is predictable, quiet, and largely independent. It matches your need for structured environments, minimal social pressure, and clear expectations.
Digital policy librarian roles are most common in larger organizations and institutions with significant regulatory or procedural demands. Opportunities cluster in metro areas with hospitals, universities, government agencies, and corporate headquarters, but remote roles are increasingly available.
Starting range: ~$70k–$100k.
Mid-career range: ~$100k–$135k (higher in heavily regulated sectors or leadership roles).
Estimates based on information management, records management, and policy documentation roles.
From here, you could move into broader knowledge management, records management, or compliance documentation leadership roles. The experience organizing and maintaining critical documents translates well to many administrative and governance positions.
Notes on Salary Sources: Approximate ranges extrapolated from information management and compliance documentation positions.
SOC proxy: TBD — similar to instructional designer / learning architect roles in the final Greg report.
Students and professionals often face a confusing mix of courses, micro-credentials, and programs with no clear path from A to B. A Learning Path Blueprint Engineer designs structured, step-by-step learning journeys that move people from starting skills to target outcomes. The role exists to bring order and clarity to increasingly complex learning ecosystems.
Traditional curriculum design often focused on individual courses or programs. Today, learners combine online modules, certifications, degrees, and on-the-job training from many sources. Instead of designing single courses, the blueprint engineer maps entire pathways—sequencing experiences, prerequisites, and assessments into coherent plans, often supported by digital platforms.
Good learning paths reduce anxiety and decision fatigue for learners who want to grow but don’t know where to start. This role supports people by providing a clear roadmap that respects their time, budget, and goals. It suits someone who wants to help others learn through structure and planning rather than high-energy teaching.
People often come from instructional design, academic advising, educational program design, or learning and development roles. Education paths include instructional design, education, psychology, or related fields, sometimes combined with analytics or information systems coursework. Experience with learning platforms and credential frameworks is particularly useful.
The work is quiet and planning-focused, often within academic, EdTech, or corporate learning teams. It follows project cycles tied to term launches, program revisions, or new initiatives. Standard business hours are typical, and remote / hybrid arrangements are increasingly common in education and training design.
You would thrive in designing structured, step-by-step learning pathways because you naturally think in linear, organized sequences. The role involves improving existing systems, which is one of your core strengths. It is quiet, methodical work that allows you to help people through structured guidance rather than high-pressure interaction. This blends your analytical style with your interest in education and career development.
Learning path design roles appear in education hubs, EdTech clusters, and corporate centers with strong training programs. Many roles are remote-friendly, especially with online learning platforms and distributed teams.
Starting range: ~$80k–$115k.
Mid-career range: ~$115k–$150k (higher in EdTech firms or large corporate learning organizations).
Estimates based on instructional design, learning architect, and program design roles with pathway responsibilities.
From this role, you could move into curriculum leadership, academic program design, corporate learning strategy, or EdTech product roles. The structured way of thinking about learning journeys is valuable across many education and training settings.
Notes on Salary Sources: Approximate ranges extrapolated from instructional design and learning architecture positions.
SOC proxy: TBD — similar to learning architect / credential framework analyst roles in the final Greg report.
Micro-credentials, badges, and short-form certificates have exploded in education and workforce development, but they are often uncoordinated and confusing. A Micro-Credential Pathway Designer organizes these offerings into coherent, stackable paths that lead to meaningful skills and employment outcomes. The role exists to bring order to a rapidly growing and fragmented credentialing landscape.
Traditional curriculum development focused on full degrees or standalone courses. Today’s credential ecosystems require careful sequencing, competency mapping, and alignment with industry standards. Instead of designing a single course, the designer builds multi-step credential frameworks that learners can navigate without getting lost.
Learners often feel overwhelmed by the sheer number of short courses and badges available. This role helps them understand what builds toward real progress and what is just noise. It quietly supports learners by giving them clarity, structure, and confidence in their advancement.
People often come from instructional design, educational policy, curriculum development, or workforce-development roles. Education paths include instructional design, education, information science, or related fields. Experience with credential frameworks, competency models, and EdTech platforms is especially helpful.
The work is quiet, analytical, and project-based, with a focus on documentation and long-term planning. Hours are predictable and align with academic or corporate project cycles. Remote and hybrid roles are increasingly common due to the digital nature of credential platforms.
You would excel at creating structured credential sequences because it involves logical planning and well-defined steps. The work allows you to improve existing systems in education, aligning with your strengths. It is quiet, predictable, and focused on clarity. You also get to help others without heavy interpersonal demands, which fits your preferences well.
Roles appear in education hubs, EdTech companies, and workforce-development organizations. Many positions are remote-friendly, especially those centered around digital credential platforms.
Starting range: ~$80k–$115k.
Mid-career range: ~$115k–$150k (higher in EdTech firms or credential-focused organizations).
Estimates based on instructional design, framework development, and credential-mapping roles.
This role can lead to positions in curriculum leadership, workforce strategy, credential design, or EdTech product development. The structured use of competencies and stackability transfers easily to many education and training contexts.
Notes on Salary Sources: Based on instructional design and credential framework roles.
SOC proxy: TBD — similar to curriculum analyst / instructional systems designer roles in the final Greg report.
Curricula across schools, colleges, and training programs can be inconsistent, outdated, or misaligned with competencies and real-world needs. An AI-Enhanced Curriculum Mapping Specialist uses structured analysis and AI tools to align learning outcomes, content, and assessments across programs. The role exists to help institutions maintain coherence and relevance in fast-changing fields.
Traditional curriculum mapping involved manually assembling documents from departments and reconciling differences. AI tools now assist by comparing outcomes, detecting redundancy, and identifying gaps. Instead of relying purely on manual review, the specialist trains AI systems to support curriculum analysis while still applying human judgment to final decisions.
This work helps educators and learners by ensuring programs are organized, logically sequenced, and fair. The specialist reduces confusion and redundancy, helping students move smoothly through a curriculum without wasted effort. It suits someone who wants to make education more coherent without directly teaching or presenting.
People often come from instructional design, academic program review, educational research, or content strategy roles. Education paths include instructional design, curriculum studies, education, or related fields. Additional training in analytics or AI-supported curriculum tools can enhance effectiveness.
The work is calm, research-oriented, and heavily documentation-focused. It follows academic cycles and project timelines. Remote and hybrid options are common, especially in EdTech and consulting contexts. The role rewards patience and deep analysis more than social interaction.
This role matches your analytical mind and your preference for structured, sequential processes. Mapping curriculum with AI assistance requires precision and clear logic—two of your strongest traits. The work is quiet, organized, and behind-the-scenes. You would enjoy aligning learning outcomes with structured plans while improving educational systems.
Opportunities cluster around education hubs, EdTech companies, and curriculum-focused agencies. Many roles are remote-friendly due to the digital nature of mapping work.
Starting range: ~$80k–$115k.
Mid-career range: ~$115k–$150k (higher in EdTech or major universities).
Estimates based on curriculum analyst, instructional systems designer, and content strategist roles.
From this role, you could move into curriculum leadership, program review, learning design, or EdTech product strategy. The structured analysis skills are highly transferable.
Notes on Salary Sources: Based on curriculum, competency mapping, and instructional systems roles.
SOC proxy: TBD — similar to learning records system administrator / portfolio systems analyst in the final Greg report.
More learners and employees are using digital credentials and skill portfolios to track their achievements. Organizations need specialists who manage these systems, ensure data accuracy, and align credentials with internal and external standards. The role exists to provide a reliable system of record for skills, learning, and verified accomplishments.
In the past, transcripts and certificates were static paper documents. Today’s digital credentials need to be stored, validated, searchable, and interoperable across platforms. The coordinator ensures that the underlying data structures and metadata remain consistent and trustworthy as credential ecosystems expand.
This role helps people showcase their skills in a clear and credible way. By maintaining accurate credential data, the coordinator makes it easier for learners, employees, and hiring managers to understand what someone knows and can do. It supports others quietly through meticulous organization and system stewardship.
Backgrounds may include learning systems administration, educational technology, records management, or information systems. Education paths include instructional technology, information science, education, or IT. Knowledge of credential frameworks and interoperability standards (e.g., Open Badges) is especially useful.
The environment is quiet, structured, and system-focused. Most work is computer-based, with occasional meetings for coordination. Hours are predictable, and remote/hybrid roles are common due to the digital nature of credential platforms.
This role fits your detail orientation and preference for structured educational systems. You would enjoy managing credential frameworks and ensuring consistency across digital portfolios. The work is predictable and analytical, supporting your interest in education and structured guidance.
Digital credential roles are most common in education hubs, EdTech companies, and large corporations adopting skills-based learning and hiring. Many roles are fully remote.
Starting range: ~$70k–$100k.
Mid-career range: ~$100k–$135k (higher for complex systems or standards-driven organizations).
Estimates based on learning systems admin, portfolio platform design, and credential data roles.
You could move into roles such as learning systems architect, credential program manager, HR/skills platform administrator, or EdTech operations. The foundation in digital records and structured competency data is widely applicable.
Notes on Salary Sources: Based on digital credential and records-management systems roles.
SOC proxy: TBD — similar to learning analytics analyst / education data implementation roles in the final Greg report.
Many schools, universities, and organizations collect learning data but fail to turn it into actionable insight. A Learning Analytics Implementation Specialist sets up the systems, dashboards, and data flows that let educators and leaders track progress and outcomes. The role exists to turn raw data into reliable, usable information for improving learning experiences.
Traditional institutional research and reporting often produced static, infrequent reports. Modern learning analytics focus on more continuous, granular data and dashboards linked directly to courses, tools, and student journeys. Instead of writing occasional reports by hand, the specialist configures systems that deliver ongoing visibility to stakeholders.
Behind the charts and dashboards are real students and learners. Done well, learning analytics can help identify who needs help sooner, which courses need revision, and where resources should go. The specialist supports people indirectly by giving decision-makers clear, accurate information without hype or guesswork.
People often come from educational technology, institutional research, data analysis, or learning systems administration. Education paths include education, information systems, statistics, or learning analytics. Training in SQL, basic programming, and specific learning platforms (LMS, LRS) is very useful.
The environment is quiet, tool-based, and project-driven. Most work happens at a desk, with meetings scheduled for requirements gathering or training. Hours are typically standard business times, and remote/hybrid work is increasingly common in analytics-related roles.
This career suits your methodical nature and preference for structured, data-driven environments. You would enjoy setting up analytics systems that track clear metrics and measurable outcomes. The work is behind-the-scenes and focused on accuracy, allowing you to improve educational processes through logic and planning rather than persuasion.
Learning analytics roles cluster around universities, EdTech firms, and education agencies, with many remote opportunities available due to the cloud-based nature of most tools.
Starting range: ~$80k–$115k.
Mid-career range: ~$115k–$150k (higher in large institutions or EdTech companies with complex data environments).
Estimates based on learning analytics, education data analyst, and BI implementation roles in education contexts.
From this role, you could move into learning analytics leadership, data engineering for education, institutional research, or product roles in EdTech firms. The skills in implementation and structured reporting remain widely valuable.
Notes on Salary Sources: Based on learning analytics and education data implementation roles.
SOC proxy: TBD — similar to process improvement analyst / continuous improvement specialist roles in the final Greg report.
Organizations often want to improve but lack clear maps of their current processes and performance. A Continuous Improvement Mapping Specialist documents how work actually happens, identifies bottlenecks, and highlights opportunities for improvement. The role exists to give leaders and teams a structured, visual starting point for ongoing change.
Traditional improvement efforts could be reactive and anecdotal, driven by complaints or isolated observations. Modern continuous improvement relies more on structured mapping, data, and repeatable methods. Instead of launching one-off initiatives, the specialist supports a steady cadence of small, evidence-based changes.
This role improves daily life for employees and customers by smoothing out frustrating processes and reducing wasteful steps. It supports people quietly by making their work environment more logical and less chaotic. It suits someone who wants to help others through analysis and planning rather than motivational speeches.
People come from operations, quality, business analysis, or industrial engineering roles. Education paths include business, operations management, industrial engineering, or related fields. Training in Lean, Six Sigma, or continuous improvement methodologies is common and helpful.
The work combines quiet desk analysis with scheduled walkthroughs and meetings. It follows project cycles with defined phases and milestones. Hours are usually standard business times, with modest travel depending on the number of locations involved.
This position matches your love of refining systems using measurable data. You would excel at mapping processes and identifying logical improvements. The work is calm, structured, and independent, leveraging your planning strengths and preference for clear, step-by-step outcomes.
Continuous improvement roles are common in regions with manufacturing, logistics hubs, healthcare systems, and large service organizations. Many roles can support multiple sites from a central or hybrid location.
Starting range: ~$75k–$110k.
Mid-career range: ~$110k–$145k (higher in large enterprises or consulting roles).
Estimates based on continuous improvement analyst, process improvement specialist, and Lean/quality roles.
From this role, you could move into continuous improvement leadership, operations management, or specialized process design consulting. The habits of mapping, measurement, and structured problem-solving remain in demand across many industries.
Notes on Salary Sources: Based on continuous improvement and process improvement positions.
SOC proxy: TBD — similar to education consultant / organizational development analyst roles in the final Greg report.
Students, schools, and career services often work with disconnected tools and uncoordinated processes. A Career Systems Architect designs the overall structure—tools, workflows, and data flows—that guide people from exploration to informed decisions. The role exists to turn scattered career resources into an integrated, reliable system.
Traditional guidance relied heavily on individual counselors, paper handouts, and one-off conversations. Today, career exploration happens through platforms, dashboards, structured assessments, and data-informed pathways. Instead of advising one student at a time, the architect designs scalable systems that support thousands of students consistently.
Behind every dashboard or pathway is a student who may feel anxious or lost about their future. The architect helps reduce that anxiety by creating clear, structured ways to explore options, compare paths, and plan next steps. It is a deeply human role, even though it operates behind the scenes at the systems level.
People may come from educational consulting, learning systems design, data-informed counseling, or product roles in EdTech. Education paths include education, psychology, information systems, or operations research, sometimes combined with analytics or UX coursework. Familiarity with assessments, career taxonomies, and student information systems is important.
The work is largely project-based and computer-focused, with scheduled meetings to gather requirements and review designs. It typically follows academic or initiative timelines rather than constant emergencies. Hybrid and remote work are common, especially when systems span multiple campuses or regions.
This role aligns closely with your long-term interest in career guidance and system-building. Designing structured career pathways lets you apply logic and detailed analysis to help people in an organized way. The work is largely independent and behind-the-scenes and allows you to bring clarity and stability to an area that is often confusing for students.
Career systems roles appear where education and workforce initiatives are active—regional education hubs, EdTech companies, and large institutions with centralized career centers. Many positions can be remote due to the software-driven nature of the work.
Starting range: ~$85k–$120k.
Mid-career range: ~$120k–$165k (higher in EdTech or large multi-campus systems).
Estimates based on education consultant, product/solutions architect in EdTech, and organizational development roles focused on career systems.
Experience in this role can lead to positions in EdTech product management, education strategy, analytics leadership, or system-level advising. The core capability—designing structured, scalable supports for student decisions—remains relevant even as specific tools change.
Notes on Salary Sources: Approximate ranges extrapolated from EdTech, education strategy, and systems design roles.
SOC proxy: TBD — similar to education data analyst / institutional research roles in the final Greg report.
Schools, colleges, and training programs are under pressure to show that their efforts lead to real outcomes—completion, employment, earnings, or other success indicators. An Education Data and Outcomes Analyst studies these patterns and helps leaders understand what is working. The role exists to turn raw education data into meaningful insight.
Traditional institutional research produced periodic summary reports for accreditation or compliance. Modern outcomes analysis is more continuous, granular, and tied to decision-making. Instead of just reporting numbers, this role focuses on clear questions, careful definitions, and repeatable analyses that can guide improvements.
Behind each data point is a student’s real life—time, money, and hopes invested in education. The analyst helps ensure that programs genuinely serve students by highlighting successes and problems using evidence. It’s a quiet way to advocate for learners without being on the front lines.
People often come from statistics, social science research, institutional research, or analytics roles. Education paths include statistics, economics, public policy, education, or data analytics. Skills in SQL, spreadsheets, BI tools, and clear reporting are central.
The environment is quiet and analysis-heavy, typically within research, analytics, or planning offices. Work follows reporting cycles and project timelines, not constant emergencies. Remote and hybrid roles are increasingly common for data-focused positions.
This position fits your desire for measurable results and structured evaluation. You would enjoy analyzing student outcomes using concrete data rather than subjective impressions. The work is quiet, methodical, and independent, aligning well with your preference for analytical, behind-the-scenes roles that still support people in a meaningful way.
Roles are common in metro areas with dense education systems, state capitals, and regions with strong higher-education presence. Many organizations now support remote analysts as long as data access is secure.
Starting range: ~$75k–$105k.
Mid-career range: ~$105k–$140k (higher in large systems or specialized analytics groups).
Estimates based on institutional research, education data analyst, and outcomes research roles.
From here, you could move into analytics leadership, policy analysis, EdTech analytics, or roles focused on specific initiatives like financial aid or workforce outcomes. The underlying quantitative analysis skills remain valuable across sectors.
Notes on Salary Sources: Based on compensation patterns for institutional research and education analytics roles.
SOC proxy: TBD — similar to student success analyst / retention strategist roles in the final Greg report.
Colleges and schools want to improve retention, completion, and student well-being, but efforts can be scattered or reactive. A Student Success Data Strategist integrates data from multiple sources to identify patterns and guide interventions. The role exists to make student success work more targeted, evidence-based, and efficient.
Traditional student support relied heavily on individual staff intuition and broad, one-size-fits-all programs. Today, institutions can track detailed signals—course performance, engagement, attendance—and use them to prioritize support. Instead of managing a single program, the strategist helps design a whole portfolio of data-informed efforts.
Each pattern the strategist analyzes represents real students who may be at risk of dropping out or falling behind. By identifying early warning signs and effective supports, this role helps more students stay on track. It supports others from behind the scenes, making it easier for front-line staff to direct their energy where it’s needed most.
Backgrounds include education data analysis, advising with strong data responsibilities, or institutional research. Education paths include education, psychology, statistics, public policy, or analytics. Skills in dashboards, SQL or similar query tools, and data visualization are important.
The role is research and analysis oriented, with regular cycles of reporting and project work. Interactions are purposeful—meeting with teams to review findings—rather than constant. Standard business hours are typical, and remote / hybrid arrangements are increasingly accepted.
This job fits your analytical strengths and desire to help individuals indirectly through structured insights. You would enjoy finding patterns in student performance data and proposing logical improvements. The work is quiet, research-driven, and methodical, providing meaningful impact without requiring heavy day-to-day interaction.
These roles appear across higher education and large school systems, especially where analytics and student success initiatives are a priority. Many institutions and EdTech providers now offer remote-friendly positions.
Starting range: ~$80k–$115k.
Mid-career range: ~$115k–$150k (higher in large systems or analytics-centered teams).
Estimates based on student success analytics, institutional research, and education strategy roles.
From this role, you could move into institutional research leadership, student success strategy, or analytics roles in EdTech and policy. The structured use of data to guide support translates across many organizations.
Notes on Salary Sources: Based on compensation for student success and education analytics positions.
SOC proxy: TBD — similar to assessment designer / educational technologist roles in the final Greg report.
Remote learning and hiring have created demand for assessments that can be delivered, monitored, and scored online—without sacrificing fairness or integrity. A Remote Assessment Systems Designer creates structured assessment pathways, tools, and rules for these environments. The role exists to make remote evaluations consistent, secure, and meaningful.
Traditional assessment design focused on in-person exams and paper-based tests. Today’s remote settings require careful attention to identity verification, proctoring options, question formats, and technical reliability. Instead of designing a single test, the systems designer builds an end-to-end process for assessment delivery and follow-up.
Assessments affect real opportunities—grades, credentials, job offers. This role protects both candidates and institutions by designing systems that are fair, clear, and as low-stress as possible while still maintaining rigor. It suits someone who prefers to help people through thoughtful system design rather than direct instruction.
People may come from instructional design, testing and measurement, educational technology, or HR assessment roles. Education paths include instructional design, education, psychology, or related fields, sometimes with psychometrics or measurement coursework. Familiarity with LMSs, assessment platforms, and security features is valuable.
The role is largely tool- and documentation-focused, with purposeful meetings to coordinate with faculty, HR, or content experts. Work follows project and testing cycles with some periods of heightened activity but generally stable schedules. Remote and hybrid work are common in this domain.
This role suits your ability to design structured, rule-based systems. You would enjoy creating assessment pathways that are logical, predictable, and well-organized. The work is independent and quiet, with clear objectives and measurable success criteria, aligning well with your preferences and strengths.
Remote assessment roles appear in education hubs, certification organizations, and companies offering global remote testing. Many positions are fully remote due to the online nature of the work.
Starting range: ~$80k–$115k.
Mid-career range: ~$115k–$150k (higher in licensing bodies or major assessment firms).
Estimates based on assessment design, testing operations, and educational technology roles.
From here, you could move into broader assessment leadership, EdTech product roles, testing operations management, or learning systems design. The skills in structured remote evaluation are likely to remain in demand.
Notes on Salary Sources: Based on remote exam design and education-assessment roles.
SOC proxy: TBD — similar to career advisor / guidance specialist roles with analytics support in the final Greg report.
Students and adults now have access to vast labor-market data and many educational options, but they still need help interpreting that information. An AI-Supported Career Pathway Advisor uses structured tools, profiles, and AI guidance to help individuals explore and choose realistic paths. The role exists to combine human judgment with data in a systematic way.
Traditional advising relied heavily on anecdotal knowledge and one-on-one conversations without much data. Modern approaches integrate assessments, outcome statistics, and AI-supported recommendations. Instead of relying purely on memory, the advisor works within a structured system that supports consistent, repeatable guidance.
This is one of the most directly student-facing roles in this group. It supports people at vulnerable decision points—choosing majors, training paths, or career changes. The advisor’s calm, structured approach can greatly reduce anxiety by turning vague worries into concrete plans grounded in real data.
People often come from counseling, academic advising, coaching, or education backgrounds, with added training in labor-market data or analytics tools. Education paths may include counseling, psychology, education, or related fields. Certificates or short courses in career development and data-informed advising are helpful.
The environment is typically quiet and appointment-based, with time split between sessions and preparation. While there is more direct interaction than some other roles here, conversations are purposeful and one-on-one rather than public-facing. Schedules follow academic or office hours with limited travel.
This role blends your interest in career guidance with your analytical approach. You would enjoy using structured tools and data to help individuals in one-on-one settings. The work is purposeful, predictable, and aligns with your desire to help others through logic rather than persuasion or sales-oriented interaction.
AI-supported advising roles appear in education hubs, workforce centers, and organizations adopting modern career platforms. Many hybrid and remote options are emerging as advising and coaching move online.
Starting range: ~$70k–$100k.
Mid-career range: ~$100k–$135k (higher in data-heavy or leadership roles, or private practice with strong demand).
Estimates based on career advisor, counselor, and analytics-supported guidance roles, adjusted for AI-enhanced practice.
From here, you could move into system-level career design, education strategy, EdTech advising tools, or specialized coaching for particular populations. The combination of data literacy and structured one-on-one guidance is valuable in many settings.
Notes on Salary Sources: Based on compensation for structured, data-informed advising and counseling roles.
SOC proxy: TBD — similar to organizational assessment designer / HR systems analyst roles in the final Greg report.
Organizations increasingly need consistent, fair, and data-informed interviewing practices. A Structured Interview Framework Designer builds standardized question banks, scoring rubrics, and evaluation workflows to reduce bias and inconsistency in hiring. The role exists to turn subjective, unpredictable interviews into reliable systems that lead to better decisions.
Traditional interviews varied widely from interviewer to interviewer, often relying on intuition. Modern hiring emphasizes evidence-based practices, structured scoring, and validation. Instead of letting every manager improvise, this role creates a system that ensures every candidate is evaluated under comparable conditions.
This role quietly protects candidates from unfair treatment and helps hiring teams avoid unintentional bias. Thoughtful structure relieves anxiety for both interviewers and applicants. It suits someone who wants to improve fairness and clarity without stepping into high-pressure interpersonal roles.
Backgrounds may include HR analytics, industrial-organizational psychology, process design, or operations. Education paths include psychology, HR, business, data analytics, or related fields. Exposure to assessment design, validation methods, or structured interviewing principles is beneficial.
The work is quiet, documentation-heavy, and project-based. Interactions are planned and purposeful—mostly training sessions or meetings to gather requirements. Hours follow normal business schedules with minimal travel and low noise.
This job fits your preference for clear guidelines, consistency, and reducing ambiguity. You would excel at designing structured interview systems that follow logical steps. The work is analytical, independent, and highly organized, and it improves systems rather than forcing you into constant interpersonal work.
Roles cluster in metro areas with large employers, government agencies, and HRTech firms. Many companies now support remote work for documentation-heavy talent systems roles.
Starting range: ~$85k–$115k.
Mid-career range: ~$115k–$150k (higher in regulated industries or HRTech companies).
Estimates based on structured interviewing, HR systems design, and industrial-organizational roles.
This role can lead to positions in talent systems architecture, HR analytics, organizational development, or consulting. The structured design skills transfer well to many process-driven roles.
Notes on Salary Sources: Based on structured interviewing and HR system-design roles.
SOC proxy: TBD — similar to EdTech product analyst / education data insights roles in the final Greg report.
EdTech platforms, career-navigation tools, and education analytics systems need specialists who can analyze user behavior, outcomes, and patterns to guide product improvements. An Education and Career Insight Product Analyst studies how students and advisors use tools and identifies opportunities to make them clearer, smarter, and more effective. The role exists to ensure these platforms actually support better decision-making.
Traditional education products were often built on intuition or anecdotal feedback. Modern tools are expected to be data-driven, user-centered, and continuously improved. Instead of waiting for annual redesigns, the analyst provides ongoing insight drawn from usage patterns, outcomes, and student success metrics.
Every product insight reflects real students trying to understand their futures. The analyst helps reduce confusion, improve navigation, and support better matches between people and opportunities. It is a behind-the-scenes role that quietly improves many lives through thoughtful refinement of digital tools.
People may come from data analysis, UX research, education technology, or product roles. Education paths include data analytics, education, psychology, UX, or information systems. Skills in BI tools, SQL or similar, and user-behavior analysis platforms are helpful.
The work is quiet and data-driven, embedded in product or analytics teams. Interaction tends to be structured—standups, planning meetings, insight reviews. Hours are regular, and remote/hybrid roles are common in tech and education-based organizations.
This role aligns with your desire to help individuals through structured information and logical insight. You would enjoy analyzing data to refine tools that guide students and educators. The work is quiet, methodical, and behind-the-scenes, letting you apply analytical strengths in a meaningful, student-centered domain.
Roles cluster in EdTech hubs, education systems, and organizations with strong analytics teams. Many positions are remote-friendly given the digital nature of the work.
Starting range: ~$85k–$120k.
Mid-career range: ~$120k–$160k (higher in well-funded EdTech firms or advanced analytics teams).
Estimates based on EdTech product analyst, UX analytics, and education insights roles.
You could move into product management, UX research, analytics leadership, or education strategy roles. The structured insight work prepares you for many product and analytics careers.
Notes on Salary Sources: Based on compensation for EdTech analytics and education insights roles.