Occupation · SOC 15-2041.00
Develop or apply mathematical or statistical theory and methods to collect, organize, interpret, and summarize numerical data to provide usable information. May specialize in fields such as biostatistics, agricultural statistics, business statistics, or economic statistics. Includes mathematical and survey statisticians.
Also called: Database Analyst · Mathematical Statistician · Statistical Analyst · Statistician · Demographer · Education Research Analyst · Psychometric Consultant · Quantitative Methodologist · Statistical Consultant · Statistical Reporting Analyst · Analytical Statistician · Applied Scientist
Job family: Computer and Mathematical Occupations
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A fast read on where AI already shows up in this occupation, where it stays a copilot, where humans remain in the loop, and what the labor market is doing. Built from observed Claude.ai conversations mapped to O*NET tasks and from published research — measures of usage and exposure, not advice or predictions that the job is going away.
Task areas most often handled directively in observed AI conversations — candidates to delegate with light review.
Task areas where people work with AI — iterating, learning, or checking — staying in the loop rather than handing the task off.
Task areas where a human was still judged necessary in a large share of observed conversations — not a safety ruling, an observed-need signal.
The capabilities O*NET rates most important for this occupation — the human ground the work is built on.
See all skills →Independent published positions, read together — not a forecast.
99th-percentile task overlap — yet about 2,000 openings a year (+8.5% projected, BLS), and observed AI use leans 5420% copilot, not hand-off (AEI) . What exposure means →
What today's research says about this occupation's exposure to AI, how AI is actually being used in it, and where employment is headed. These are positions within published studies — measures of exposure and usage, not predictions that this job will disappear.
Each study uses its own scale, so the raw scores are not comparable across rows — the percentile (this job's rank among all U.S. occupations with data) is the comparable figure, and sizes the bars.
| Measure | Rank vs all occupations | Percentile | Score |
|---|---|---|---|
| Overall AI exposure (Felten et al.) High | 95th | 1.4 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 95th | 1.0 | |
| AI assistant applicability (Microsoft) High | 92nd | 0.3 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.6), with simple added tooling (β 0.8), and including AI-powered software (γ 1.0). Higher means more of the job's tasks could be done at least twice as fast — not that they will be automated away.
Most of this job's tasks can be done remotely (Dingel–Neiman), which tends to track with higher digital and AI exposure.
A pre-LLM (2013) estimate of how automatable this job is by computerization and robotics. Shown for historical context only — it is not part of any current AI ranking.
Frey–Osborne probability 0.2 · 34th percentile among occupations · Moderate
Among measured AI assistant conversations mapped to this occupation (Anthropic Economic Index, 2026-01-15), these task types came up most. These are shares of observed AI conversations — not shares of the job, of worker time, or of what could be automated.
| Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data. | 10.0% | |
| Report results of statistical analyses, including information in the form of graphs, charts, and tables. | 7.6% | |
| Process large amounts of data for statistical modeling and graphic analysis, using computers. | 5.7% | |
| Identify relationships and trends in data, as well as any factors that could affect the results of research. | 5.3% | |
| Report results of statistical analyses in peer-reviewed papers and technical manuals. | 2.2% | |
| Develop and test experimental designs, sampling techniques, and analytical methods. | 0.5% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Growing fast · +8.5% by 2034 |
| Projected annual openings | 2,000 |
| Employment 2024 → 2034 | 32,200 → 34,900 |
“Annual openings” counts new jobs plus replacements for workers who leave the occupation, so it can be large even when growth is modest.
The ILO's 2025 global study scores generative-AI exposure on the international ISCO-08 occupation system, not US SOC. Bridged through the published (and approximate, many-to-many) IBS O*NET-SOC ↔ ISCO-08 crosswalk, this US occupation corresponds to the international occupation below. Exposure here means how much of the work's tasks today's AI can attempt — task overlap, not automation, adoption, or jobs lost.
| International occupation (ISCO-08) | Task exposure (2025) | Most tasks fall in |
|---|---|---|
| Mathematicians, Actuaries and Statisticians · 2120 | 56% | Gradient 3 |
Read the whole six-band gradient on the GenAI exposure gradient page. The crosswalk is approximate: a US occupation can map to several international ones, and the ILO scores describe the international occupation, not this exact US role.
How people actually apply AI to this occupation's tasks, from Claude.ai (Free and Pro) conversations in the Anthropic Economic Index, 2026-01-15. This is one AI assistant's consumer sample — not all AI, not the whole workforce. Autonomy and the collaboration mix are model-rated estimates; figures below the sample floor are hidden.
| Augmentation vs. automation | 54.2% working with AI · 39.5% handed to AI |
| Most common way people use AI here | Directive · AI does it; you give the instruction |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 34.0% |
The role's most common tasks in AI conversations, each tagged with how people work with the AI on it. “Usage” is the share of observed conversations, not of the job.
| Task | How | Usage |
|---|---|---|
| Evaluate sources of information to determine any limitations in terms of reliability or usability. | Validation | 5.5% |
| Prepare data for processing by organizing information, checking for any inaccuracies, and adjusting and weighting the raw data. | Directive | 4.2% |
| Report results of statistical analyses, including information in the form of graphs, charts, and tables. | Directive | 3.5% |
| Develop and test experimental designs, sampling techniques, and analytical methods. | Learning | 2.1% |
| Develop an understanding of fields to which statistical methods are to be applied to determine whether methods and results are appropriate. | Learning | 1.9% |
| Develop software applications or programming to use for statistical modeling and graphic analysis. | Directive | 1.6% |
| Design research projects that apply valid scientific techniques and use information obtained from baselines or historical data to structure uncompromised and efficient analyses. | Directive | 1.5% |
| Report results of statistical analyses in peer-reviewed papers and technical manuals. | Directive | 1.4% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Evaluate sources of information to determine any limitations in terms of reliability or usability. | 97.4% | |
| Prepare data for processing by organizing information, checking for any inaccuracies, and adjusting and weighting the raw data. | 92.3% | |
| Examine theories, such as those of probability and inference, to discover mathematical bases for new or improved methods of obtaining and evaluating numerical data. | 87.9% | |
| Develop an understanding of fields to which statistical methods are to be applied to determine whether methods and results are appropriate. | 87.8% | |
| Design research projects that apply valid scientific techniques and use information obtained from baselines or historical data to structure uncompromised and efficient analyses. | 84.9% | |
| Report results of statistical analyses, including information in the form of graphs, charts, and tables. | 83.3% |
Example prompts phrased from the tasks people most often delegate to AI in this occupation (Anthropic Economic Index). Each shows the underlying measured task and its share of observed AI use. They are suggested phrasings of real tasks — starting points, not endorsed instructions.
Help me evaluate sources of information to determine any limitations in terms of reliability or usability. From: Evaluate sources of information to determine any limitations in terms of reliability or usability. · 5.5% of measured AI use · validation
Help me prepare data for processing by organizing information, checking for any inaccuracies, and adjusting and weighting the raw data. From: Prepare data for processing by organizing information, checking for any inaccuracies, and adjusting and weighting the raw data. · 4.2% of measured AI use · directive
Help me report results of statistical analyses, including information in the form of graphs, charts, and tables. From: Report results of statistical analyses, including information in the form of graphs, charts, and tables. · 3.5% of measured AI use · directive
Help me develop and test experimental designs, sampling techniques, and analytical methods. From: Develop and test experimental designs, sampling techniques, and analytical methods. · 2.1% of measured AI use · learning
All 19 tasks O*NET lists for this occupation, ordered by importance. Each links to its own page with AI-exposure and observed-use detail.
O*NET importance rating, from 1 (not important) to 5 (extremely important).
| Mathematics | 4.9 | |
| Reading Comprehension | 4.0 | |
| Critical Thinking | 4.0 | |
| Active Listening | 3.9 | |
| Speaking | 3.9 | |
| Writing | 3.8 | |
| Active Learning | 3.8 | |
| Science | 3.5 | |
| Learning Strategies | 3.1 | |
| Monitoring | 3.0 |
| Mathematical Reasoning | 4.8 | |
| Number Facility | 4.4 | |
| Written Comprehension | 4.1 | |
| Oral Comprehension | 4.0 | |
| Oral Expression | 4.0 | |
| Written Expression | 4.0 | |
| Inductive Reasoning | 4.0 | |
| Near Vision | 4.0 | |
| Deductive Reasoning | 3.9 | |
| Information Ordering | 3.8 | |
| Category Flexibility | 3.6 | |
| Speech Clarity | 3.6 | |
| Fluency of Ideas | 3.4 | |
| Problem Sensitivity | 3.3 | |
| Speech Recognition | 3.1 | |
| Originality | 3.0 | |
| Flexibility of Closure | 3.0 |
| Mathematics | 4.7 | |
| Computers and Electronics | 4.2 | |
| English Language | 3.9 | |
| Education and Training | 2.9 |
| Complex Problem Solving | 3.9 | |
| Judgment and Decision Making | 3.4 | |
| Programming | 3.0 | |
| Systems Analysis | 3.0 | |
| Systems Evaluation | 3.0 | |
| Time Management | 3.0 | |
| Coordination | 2.9 | |
| Instructing | 2.9 | |
| Operations Analysis | 2.9 |
Skills employers ask for in job postings for this occupation (Lightcast), with whether each is a common or specialized skill.
Showing the top 40 of 47.
Showing the top 40 of 70.
How characteristic each condition is of the job, on O*NET's 1–5 context scale (higher = more present in day-to-day work). Each condition links to how it varies across all occupations.
What to study: Biological and Biomedical Sciences , Business, Management, Marketing, and Related Support Services , Education , Mathematics and Statistics , Multi/Interdisciplinary Studies , Psychology , Social Sciences . Fields of study crosswalked to this occupation (NCES CIP–SOC), not a requirement.
Share of people in this occupation at each level of education.
| Master's Degree | 61.9% | |
| Bachelor's Degree | 14.3% | |
| Doctoral Degree | 9.5% | |
| Some College Courses | 4.8% | |
| Associate's Degree (or other 2-year degree) | 4.8% | |
| Post-Doctoral Training | 4.8% |
The interests and personal qualities O*NET associates with people who do this work.
| Mathematics/Statistics | 7.0 | |
| Information Technology | 4.1 | |
| Medical Science | 3.1 | |
| Finance | 2.8 | |
| Accounting | 2.7 | |
| Office Work | 2.5 | |
| Life Science | 2.5 | |
| Social Science | 2.4 | |
| Public Speaking | 2.4 |
| Investigative | 6.6 | |
| Conventional | 6.0 |
| Dependability | 6.0 | |
| Attention to Detail | 5.0 | |
| Integrity | 4.0 | |
| Cautiousness | 3.0 | |
| Intellectual Curiosity | 2.7 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $60,390 |
| 25th percentile | $79,210 |
| Median (50th) | $103,300 |
| 75th percentile | $137,610 |
| 90th percentile | $170,700 |
| People employed | 29,800 |
Where these workers are employed, by number of jobs (national, BLS OEWS). Pay shown is the occupation's national median, not industry-specific.
| Industry | Workers | National median pay |
|---|---|---|
| Professional, Scientific, and Technical Services · Sector | 9,920 | $106,470 |
| Educational Services · Sector | 3,790 | $81,810 |
| Health Care and Social Assistance · Sector | 2,530 | $99,540 |
| Management of Companies and Enterprises · Sector | 1,430 | $117,400 |
| Finance and Insurance · Sector | 1,270 | $100,720 |
| Information · Sector | 750 | $99,380 |
| Manufacturing · Sector | 730 | $140,490 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 580 | $104,000 |
| Wholesale Trade · Sector | 530 | $155,640 |
| Research and Development in the Social Sciences and Humanities · National industry | 400 | $94,100 |
| Temporary Help Services · National industry | 350 | $102,870 |
| Other Services (except Public Administration) · Sector | 320 | $87,550 |
Industries where this occupation is far more common than in the economy as a whole. The location quotient is how many times more concentrated it is here (a value of 5 means five times its economy-wide share).
| Industry | Concentration | Workers |
|---|---|---|
| Research and Development in the Social Sciences and Humanities · National industry | 34.05× | 400 |
| Professional, Scientific, and Technical Services · Sector | 4.77× | 9,920 |
| Direct Health and Medical Insurance Carriers · National industry | 3.34× | 290 |
| Management of Companies and Enterprises · Sector | 2.63× | 1,430 |
| Educational Services · Sector | 1.44× | 3,790 |
| Information · Sector | 1.33× | 750 |
| Finance and Insurance · Sector | 1.06× | 1,270 |
| Temporary Help Services · National industry | 0.68× | 350 |
Part of the Advanced Manufacturing , Digital Technology , Education , Financial Services , Healthcare & Human Services , Management & Entrepreneurship , Marketing & Sales and Public Service & Safety career clusters.
Side-by-side comparisons place two occupations’ pay, preparation, skills, and AI exposure on the same page — same data, same scale, no forecast.
Options the data surfaces for Statisticians — not advice or a forecast. Each is a real cross-link you can follow into the evidence.
Capabilities this work builds that are used across many other occupations.
Occupations O*NET rates as related — the nearby moves on the map.
How people typically prepare for this work.
On the global GenAI exposure gradient this work sits around the 94th percentile of 427 international occupations.
Statisticians show 99th-percentile AI task overlap — and about 2,000 annual U.S. openings
Statisticians show 99th-percentile AI task overlap — and about 2,000 annual U.S. openings • Statisticians rank in the 99th percentile (High band) for AI task overlap across U.S. occupations — a measure of how much of the work today's AI can attempt, not how much is automated. (Eloundou et al. (GPTs are GPTs) + Felten AIOE) • The occupation is projected to see about 2,000 U.S. job openings per year (2024–34), counting growth and replacement — a labor-demand projection made independently of AI. (BLS Employment Projections 2024–34) • BLS projects employment to be growing fast (+8.5%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $103,300, across about 29,800 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 54% looks like augmentation (drafting, iterating, checking) rather than hands-off automation — from a Claude.ai usage sample, not a census. (2026-01-15-v4-plus-2025-03-27-v2) Source: Singulariki — "Statisticians". https://singulariki.com/roles/role-15-2041-00 Note: AI task overlap measures what today's AI can attempt, not automation, job loss, or a forecast.
AssetsShare imageMethodology & sourcesPress & newsroomThe newsroom
Every line is built only from figures this page already shows and cites. AI task overlap means what today's AI can attempt — not automation, job loss, or a forecast.
Every figure above traces to a named public dataset and the exact release below — not hand-written opinion. See the full methodology for what each measure does and does not mean.
Data compiled June 2, 2026. Figures are estimates, not advice.
Singulariki. "Statisticians." Singulariki: a source-backed encyclopedia of work. Built from O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026. https://singulariki.com/roles/role-15-2041-00
Singulariki. (2026). Statisticians. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-15-2041-00
@misc{singulariki-role-15-2041-00,
title = {Statisticians},
author = {{Singulariki}},
year = {2026},
note = {O*NET 30.3; BLS Occupational Employment and Wage Statistics (OEWS) May 2024; BLS Employment Projections 2024–2034; Anthropic Economic Index v4 (2026-01-15) + v2 (2025-03-27); Microsoft “Working with AI” working-with-ai; “GPTs are GPTs” (Eloundou et al.) arXiv 2303.10130; AI Occupational Exposure (AIOE) Felten, Raj & Seamans; ILO / Gmyrek et al. GenAI exposure gradient 2025; IBS O*NET-SOC ↔ ISCO-08 occupation crosswalk 2022; Frey & Osborne (2013) frey-osborne-automation; Dingel & Neiman (2020) dingel-neiman-workathome. Accessed June 7, 2026},
url = {https://singulariki.com/roles/role-15-2041-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.