Share and intensity of work current AI systems can materially affect.
SOC 15-2041
Statisticians AI displacement risk
Computation, visualization, and report drafting are heavily AI-accelerated. Statistical judgment — choosing valid methods, designing studies, questioning data quality, and defending inference — is precisely what distinguishes statisticians from generated output.
Likely potential for exposed tasks to move to software after workflow integration.
Unlike production data science, this role's value is methodological. AI raises the cost of shallow analysis, which strengthens demand for people who can evaluate whether an analysis is actually valid.
Distribution
Where Statisticians sits across 620 tracked roles
Displacement pressure 40 — higher than 67% of the 620 occupations tracked on displacement.ai.
Score version
This page uses Seed model v0.4 (seed-v0.4-2026-05), last reviewed 2026-08-08. Directional occupation-level planning model using hand-reviewed public research, task exposure estimates, wage context, and transition-pathway assumptions.
30 O*NET task statements matched to SOC 15-2041. The displayed task profile combines these official task statements with the current public score model.
Median wage context: $105,650 (May 2025, US national). The latest BLS row matched SOC 15-2041.
Scores are planning signals, not forecasts. Local hiring demand, employer-specific workflows, licensing, and credentials must be validated before making career decisions.
2030 economic stress test
How Anthropic's scenarios classify Statisticians
SOC 15-2041 places this role in the paper's cognitive occupation group. These group-level outcomes do not change the 40/100 role score and are not an occupation forecast.
+0.4% group wage
-0.5% cognitive employment since mid-2026; 2.9% cognitive unemployment.
Economy-wide: +1.6% GDP and 3.9% unemployment.
-0.3% group wage
-3.9% cognitive employment since mid-2026; 4.5% cognitive unemployment.
Economy-wide: +8.3% GDP and 4.6% unemployment.
-11.5% group wage
-21.5% cognitive employment since mid-2026; 17.9% cognitive unemployment.
Economy-wide: +32.4% GDP and 11.9% unemployment.
Compare the assumptions and limitations across all three scenarios. Source: The Anthropic Institute Working Paper No. 2026-02.
Official task evidence
O*NET task matches for Statisticians
The current evidence import matched 30 task statements from Task Statements 31.0 (August 2026). These rows are used as a grounding layer for judging which parts of the occupation are repeatable, language-heavy, analytical, social, physical, or compliance-sensitive.
Dataset31.0 (August 2026)
Matched tasks30
SOC15-2041
- Core task / ID 8956
Analyze and interpret statistical data to identify significant differences in relationships among sources of information.
- Core task / ID 8958
Evaluate the statistical methods and procedures used to obtain data to ensure validity, applicability, efficiency, and accuracy.
- Core task / ID 8953
Report results of statistical analyses, including information in the form of graphs, charts, and tables.
- Core task / ID 21100
Determine whether statistical methods are appropriate, based on user needs or research questions of interest.
- Core task / ID 8957
Prepare data for processing by organizing information, checking for inaccuracies, and adjusting and weighting the raw data.
- Core task / ID 8966
Develop and test experimental designs, sampling techniques, and analytical methods.
Source: O*NET Resource Center, Task Statements. Raw import target: data/raw/onet/task-statements-31-0.txt.
Task profile
Where AI changes the work
Analyze and interpret statistical data
Exposure 72, automation 42%, augmentation 74%.
O*NET evidence: Analyze and interpret statistical data to identify significant differences in relations... (ID 8956)
Report results with charts and tables
Exposure 78, automation 46%, augmentation 72%.
O*NET evidence: Report results of statistical analyses, including information in the form of graphs, ch... (ID 8953)
Design studies and sampling methods
Exposure 44, automation 16%, augmentation 62%.
O*NET evidence: Develop and test experimental designs, sampling techniques, and analytical methods. (ID 8966)
Evaluate method validity for questions
Exposure 40, automation 14%, augmentation 58%.
O*NET evidence: Evaluate the statistical methods and procedures used to obtain data to ensure validity,... (ID 8958)
TaskExposureAutomationAugmentation
Analyze and interpret statistical data7242%74%
Report results with charts and tables7846%72%
Design studies and sampling methods4416%62%
Evaluate method validity for questions4014%58%
Transition pathways
Adjacent moves that preserve existing skills
credentialed transition
ModerateBiostatistician
Training horizon: 6-12 months. Skill overlap 74. Wage preservation signal 106.
- Learn clinical trial methods
- Study regulatory statistics
- Build a healthcare analysis portfolio
role redesign
ModerateResearch Methodologist
Training horizon: 3-6 months. Skill overlap 76. Wage preservation signal 100.
- Own study design reviews
- Audit AI-generated analyses
- Set evidence quality standards
Comparison guides
Compare the next move before you commit
6-12 months
Statisticians to Biostatistician
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Statisticians into Biostatistician.
3-6 months
Statisticians to Research Methodologist
Compare AI displacement pressure, wage preservation, skill overlap, training time, and first proof project for moving from Statisticians into Research Methodologist.
What the AI risk score means for Statisticians
The displacement pressure score for Statisticians is 40. That score blends task exposure, automation pressure, augmentation potential, wage vulnerability, transition feasibility, and source confidence. It is designed to help workers and workforce teams decide where to act first, not to claim a specific date when a job will disappear.
For this role, the clearest risk pattern is visible at the task level. Report results with charts and tables carries 46% automation pressure, while Analyze and interpret statistical data carries 74% augmentation potential. That means the best response is usually a targeted redesign of work: move away from repeatable production tasks and toward judgment, exception handling, coordination, stakeholder context, and accountable use of AI tools.
Labor-market context and wage risk
Median wage: $105,650 (May 2025, US national). Employment context: Methods-focused analysis profession across research and industry. Typical education: Master's degree common.
Wage vulnerability is 24, while transition feasibility is 74. A high wage-vulnerability score means workers should pay close attention to salary preservation before making a move. A high transition-feasibility score means there are adjacent paths that can reuse existing skills without requiring a complete career reset.
- Moderate displacement pressure
- Methodological judgment is the moat
- Computation is fully augmented
Upskilling priorities
Skills that make this role more resilient
The safest upskilling plan starts with skills already close to the work. For Statisticians, the strongest near-term skill priorities are listed below. These are useful whether the goal is to stay in the role, move to a redesigned version of the role, or transition into an adjacent occupation.
Experimental design
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Inference judgment
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
Statistical programming
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
AI output validation
Build proof of this skill through a work sample, checklist, dashboard, case note, workflow map, or portfolio artifact tied to the transition paths on this page.
90-day transition plan
The most practical next step is not to wait for a layoff or a full role redesign. Use the next 90 days to create evidence that you can operate in a safer, more AI-augmented version of the work.
- In the first 30 days, document the repetitive tasks in your current work and identify where AI can reduce drafting, lookup, classification, or reporting time.
- By 60 days, complete one small project connected to Biostatistician, such as learn clinical trial methods.
- By 90 days, compare internal openings and external postings for Biostatistician or Research Methodologist and update your resume around measurable workflow outcomes.
FAQ
Questions about AI and Statisticians
Will AI replace Statisticians?
Computation, visualization, and report drafting are heavily AI-accelerated. Statistical judgment — choosing valid methods, designing studies, questioning data quality, and defending inference — is precisely what distinguishes statisticians from generated output. The better planning signal is not full replacement, but which tasks become automated, which tasks become AI-assisted, and which responsibilities still need human judgment.
Which parts of Statisticians work are most exposed to AI?
Report results with charts and tables and Analyze and interpret statistical data show the strongest automation pressure in this model. Analyze and interpret statistical data and Report results with charts and tables are better treated as AI-augmented work.
What should Statisticians learn next?
Start with Experimental design, Inference judgment, Statistical programming. The most practical adjacent paths in this model are Biostatistician and Research Methodologist.
How should this score be used?
Use it as a planning signal, not a prediction. Confirm local hiring demand, wages, licensing, credentials, and employer adoption before making a career move.
Sources
Evidence trail
International Labour Organization / 20 May 2025Generative AI and Jobs: A Refined Global Index of Occupational ExposureMcKinsey Global Institute / 26 July 2023Generative AI and the Future of Work in AmericaO*NET Resource Center / Database 31.0 (August 2026)Occupation, Task, Skill, and Work-Activity DataUS Bureau of Labor Statistics / May 2025 estimates (released 15 May 2026)Occupational Employment and Wage Statistics