Statistician (15-2041.00)
Career Family
Computer & Mathematical → Statistics
Fit Summary
Investigative; MBTI: INTJ/ISTJ/INFP—analytical, rigorous, and communication‑minded.
Career Overview
Design studies and experiments, select models, analyze data, quantify uncertainty, and communicate findings; collaborate with scientists, engineers, and policy leaders.
Credential Pathways
Typical Education: Master’s or PhD in statistics/biostatistics or related; strong computing and methods training (Job Zone 5).
Pathways: Analyst → statistician/biostatistician → senior/principal → lead/mgr; adjacent paths to data science/ML.
Regulatory Moat: Advanced methods expertise, domain specialization, and reproducible research portfolio.
Alternative Pathways: Mathematicians, economists, or data scientists formalizing with statistics degrees.
Environment & Lifestyle
- Work Environment: Research labs, pharma/clinical trials, tech/finance, and government; publication and regulatory timelines.
- Sensory/Social Load: Low‑to‑moderate—meetings and presentations.
- Physicality/Fieldwork: Low—desk‑based.
- Geographic Anchoring: Every region; hubs in research/biotech/government centers; remote common.
- Remote Amenability: Very high—analysis/writing remote‑friendly.
Future-Proofing Snapshot
- AI Augmentation Potential: Very high — modeling assistance and literature synthesis; simulation at scale.
- AI Displacement Risk: Low — study design, assumptions, and interpretation remain human‑core.
- AI New Task Creation: Causal inference, ML‑ops for experimentation, and safety/effectiveness analytics.
- AI Skill Shift Intensity: High — coding, computation, and communication with AI tools.
- Automation Risk Score: Low — creative, judgement‑heavy work.
- Human-Core Score: Strong — ethics and inference under uncertainty.
- Overall Vulnerability/Resilience: Excellent prospects across domains; continuous learning expected.
- Emerging Trends: Bayesian methods, causal inference, and integrated ML/statistics workflows.
Risks / Watchpoints
- Misinterpretation and misuse of models
- Regulatory scrutiny/validation demands
- Communication gaps with stakeholders
Notes on Fit
Ideal for Investigative students who enjoy turning data into defensible decisions.
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