Operations Research Analyst (15-2031.00)

Career Family

Computer & Mathematical → Analytics & Optimization

Fit Summary

Investigative-Conventional-Enterprising; MBTI: INTJ/ENTJ—modeling, experimentation, and stakeholder translation.

Career Overview

Use math/statistics/optimization/simulation to improve decisions in logistics, pricing, scheduling, and risk; build and validate models, run experiments, and communicate recommendations.

Credential Pathways

Typical Education: Bachelor’s to master’s/PhD in OR, math, stats, or engineering; programming/solver tools (Job Zone 5).

Pathways: Analyst → senior → staff/principal → analytics/ops science leader; routes to data science/product ops.

Regulatory Moat: Rigor in experimentation, data governance, and model risk management.

Alternative Pathways: Industrial engineers or data scientists shifting to optimization roles.

Environment & Lifestyle

• Work Environment: Corporate/consulting/government; project cycles with sprints and reviews.
• Sensory/Social Load: Moderate—deadline/project pressure and stakeholder meetings.
• Physicality/Fieldwork: Low—desk‑based.
• Geographic Anchoring: Broad across industries; hubs in tech/consulting/manufacturing/logistics.
• Remote Amenability: High—analysis and collaboration tools enable distributed teams.

Future-Proofing Snapshot

• AI Augmentation Potential: Very high — model generation, feature discovery, and simulation speedups.
• AI Displacement Risk: Low-to-medium — routine analytics automate; problem framing and integration remain.
• AI New Task Creation: Digital twins, experimentation platforms, and prescriptive analytics products.
• AI Skill Shift Intensity: High — coding, MLOps, and domain modeling.
• Automation Risk Score: Medium — basic analysis commoditizes; advanced OR remains niche.
• Human-Core Score: Strong — abstraction, tradeoff design, and communication.
• Overall Vulnerability/Resilience: Resilient with growing analytics adoption.
• Emerging Trends: Causal inference, reinforcement learning, and optimization at scale.

Risks / Watchpoints

• Model–reality mismatch
• Poor data quality
• Change management blocking adoption

Notes on Fit

Excellent for Investigative students who like math for real‑world operations and enjoy explaining results.

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