Occupation · SOC 15-2031.00
Formulate and apply mathematical modeling and other optimizing methods to develop and interpret information that assists management with decisionmaking, policy formulation, or other managerial functions. May collect and analyze data and develop decision support software, services, or products. May develop and supply optimal time, cost, or logistics networks for program evaluation, review, or implementation.
Also called: Analytical Strategist · Operations Research Analyst (Ops Research Analyst) · Operations Research Scientist (Ops Research Scientist) · Researcher · Advanced Analytics Associate · Decision Analyst · Optimization Analyst · Analytics Consultant · Business Analyst · Business Operations Analyst · Business Process Analyst · Continuous Improvement Specialist
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 9,600 openings a year (+21.5% projected, BLS), and observed AI use leans 5523% 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 | 96th | 1.4 | |
| LLM task exposure, γ (OpenAI / Eloundou) High | 95th | 1.0 | |
| AI assistant applicability (Microsoft) High | 90th | 0.3 |
OpenAI's exposure study scores tasks three ways: with a language model alone (α 0.3), with simple added tooling (β 0.6), 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 0.0 · 20th percentile among occupations · Low
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.
| Prepare management reports defining and evaluating problems and recommending solutions. | 5.4% | |
| Analyze information obtained from management to conceptualize and define operational problems. | 2.3% | |
| Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters. | 1.9% | |
| Study and analyze information about alternative courses of action to determine which plan will offer the best outcomes. | 1.2% | |
| Collaborate with senior managers and decision makers to identify and solve a variety of problems and to clarify management objectives. | 0.9% | |
| Collaborate with others in the organization to ensure successful implementation of chosen problem solutions. | 0.8% |
Independent U.S. Bureau of Labor Statistics employment projection for 2024–2034 — a labor-market forecast, not an AI-impact forecast.
| Outlook | Growing fast · +21.5% by 2034 |
| Projected annual openings | 9,600 |
| Employment 2024 → 2034 | 112,100 → 136,200 |
“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 | 55.2% working with AI · 40.6% handed to AI |
| Most common way people use AI here | Iteration · you and AI go back and forth |
| Typical AI autonomy | 4.0 / 5 · higher = AI acts more independently |
| Used for work (vs. personal / coursework) | 71.1% |
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 |
|---|---|---|
| Prepare management reports defining and evaluating problems and recommending solutions. | Iteration | 9.7% |
| Break systems into their component parts, assign numerical values to each component, and examine the mathematical relationships between them. | Feedback loop | 7.4% |
| Develop business methods and procedures, including accounting systems, file systems, office systems, logistics systems, and production schedules. | Iteration | 7.3% |
| Analyze information obtained from management to conceptualize and define operational problems. | Iteration | 5.3% |
| Study and analyze information about alternative courses of action to determine which plan will offer the best outcomes. | Iteration | 3.1% |
| Collaborate with senior managers and decision makers to identify and solve a variety of problems and to clarify management objectives. | Iteration | 2.9% |
| Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters. | Directive | 0.9% |
| Define data requirements and gather and validate information, applying judgment and statistical tests. | — | 0.4% |
Tasks where the model most often judged that a person remained necessary — a useful read on the current boundary, not a guarantee.
| Collaborate with senior managers and decision makers to identify and solve a variety of problems and to clarify management objectives. | 96.9% | |
| Study and analyze information about alternative courses of action to determine which plan will offer the best outcomes. | 96.1% | |
| Analyze information obtained from management to conceptualize and define operational problems. | 93.4% | |
| Prepare management reports defining and evaluating problems and recommending solutions. | 93.3% | |
| Define data requirements and gather and validate information, applying judgment and statistical tests. | 92.3% | |
| Develop business methods and procedures, including accounting systems, file systems, office systems, logistics systems, and production schedules. | 88.4% |
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 prepare management reports defining and evaluating problems and recommending solutions. From: Prepare management reports defining and evaluating problems and recommending solutions. · 9.7% of measured AI use · task iteration
Help me break systems into their component parts, assign numerical values to each component, and examine the mathematical relationships between them. From: Break systems into their component parts, assign numerical values to each component, and examine the mathematical relationships between them. · 7.4% of measured AI use · feedback loop
Help me develop business methods and procedures, including accounting systems, file systems, office systems, logistics systems, and production schedules. From: Develop business methods and procedures, including accounting systems, file systems, office systems, logistics systems, and production schedules. · 7.3% of measured AI use · task iteration
Help me analyze information obtained from management to conceptualize and define operational problems. From: Analyze information obtained from management to conceptualize and define operational problems. · 5.3% of measured AI use · task iteration
All 16 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 | 4.7 | |
| Computers and Electronics | 4.1 | |
| Engineering and Technology | 4.0 | |
| Production and Processing | 3.7 | |
| English Language | 3.6 | |
| Design | 3.1 | |
| Education and Training | 3.0 |
| Mathematics | 4.5 | |
| Reading Comprehension | 4.0 | |
| Active Listening | 4.0 | |
| Writing | 4.0 | |
| Speaking | 4.0 | |
| Critical Thinking | 4.0 | |
| Active Learning | 3.9 | |
| Science | 3.3 | |
| Learning Strategies | 3.0 |
| Mathematical Reasoning | 4.5 | |
| Deductive Reasoning | 4.1 | |
| Inductive Reasoning | 4.1 | |
| Oral Comprehension | 4.0 | |
| Written Comprehension | 4.0 | |
| Oral Expression | 4.0 | |
| Written Expression | 4.0 | |
| Problem Sensitivity | 4.0 | |
| Number Facility | 4.0 | |
| Information Ordering | 3.9 | |
| Near Vision | 3.8 | |
| Fluency of Ideas | 3.6 | |
| Originality | 3.6 | |
| Category Flexibility | 3.6 | |
| Speech Clarity | 3.3 | |
| Flexibility of Closure | 3.1 | |
| Speech Recognition | 3.1 |
| Complex Problem Solving | 4.1 | |
| Judgment and Decision Making | 3.9 | |
| Systems Analysis | 3.9 | |
| Systems Evaluation | 3.9 | |
| Operations Analysis | 3.8 | |
| Coordination | 3.1 | |
| Time Management | 3.1 |
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 69.
Showing the top 40 of 114.
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: Business, Management, Marketing, and Related Support Services , Engineering . 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 | 42.9% | |
| Bachelor's Degree | 33.3% | |
| Doctoral Degree | 14.3% | |
| Post-Baccalaureate Certificate | 9.5% |
The interests and personal qualities O*NET associates with people who do this work.
| Dependability | 7.0 | |
| Attention to Detail | 6.0 | |
| Integrity | 5.0 | |
| Cautiousness | 4.0 | |
| Intellectual Curiosity | 3.0 |
| Mathematics/Statistics | 6.4 | |
| Information Technology | 3.5 | |
| Management/Administration | 2.8 | |
| Accounting | 2.8 | |
| Business Initiatives | 2.7 | |
| Finance | 2.6 | |
| Office Work | 2.6 | |
| Public Speaking | 2.5 |
| Investigative | 6.3 | |
| Conventional | 5.8 | |
| Enterprising | 3.1 |
U.S. · annual wages (BLS OEWS)
| 10th percentile | $53,910 |
| 25th percentile | $66,910 |
| Median (50th) | $91,290 |
| 75th percentile | $124,120 |
| 90th percentile | $159,280 |
| People employed | 107,760 |
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 |
|---|---|---|
| Finance and Insurance · Sector | 25,040 | $82,790 |
| Professional, Scientific, and Technical Services · Sector | 24,310 | $99,600 |
| Management of Companies and Enterprises · Sector | 11,770 | $96,310 |
| Educational Services · Sector | 6,770 | $74,520 |
| Information · Sector | 6,140 | $97,970 |
| Administrative and Support and Waste Management and Remediation Services · Sector | 5,080 | $80,040 |
| Manufacturing · Sector | 4,610 | $107,360 |
| Direct Health and Medical Insurance Carriers · National industry | 3,880 | $89,620 |
| Health Care and Social Assistance · Sector | 3,850 | $78,670 |
| Wholesale Trade · Sector | 2,720 | $101,800 |
| Temporary Help Services · National industry | 2,200 | $70,670 |
| Research and Development in the Social Sciences and Humanities · National industry | 1,300 | $105,940 |
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 | 30.6× | 1,300 |
| Direct Health and Medical Insurance Carriers · National industry | 12.36× | 3,880 |
| Management of Companies and Enterprises · Sector | 5.99× | 11,770 |
| Finance and Insurance · Sector | 5.75× | 25,040 |
| Fossil Fuel Electric Power Generation · National industry | 3.81× | 190 |
| Professional, Scientific, and Technical Services · Sector | 3.23× | 24,310 |
| Information · Sector | 3.02× | 6,140 |
| Utilities · Sector | 2.79× | 1,130 |
Part of the Management & Entrepreneurship career cluster.
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 Operations Research Analysts — 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.
Operations Research Analysts show 99th-percentile AI task overlap — and about 9,600 annual U.S. openings
Operations Research Analysts show 99th-percentile AI task overlap — and about 9,600 annual U.S. openings • Operations Research Analysts 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 9,600 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 (+21.5%) from 2024 to 2034. (BLS Employment Projections 2024–34) • Median annual pay is $91,290, across about 107,760 U.S. workers. (BLS OEWS (May 2024)) • Of the AI use actually observed for this work, 55% 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 — "Operations Research Analysts". https://singulariki.com/roles/role-15-2031-00 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. "Operations Research Analysts." 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-15-2031-00
Singulariki. (2026). Operations Research Analysts. Singulariki: a source-backed encyclopedia of work. Retrieved June 7, 2026, from https://singulariki.com/roles/role-15-2031-00
@misc{singulariki-role-15-2031-00,
title = {Operations Research Analysts},
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-15-2031-00}
} Citations name the underlying public dataset releases — they reflect what this page is built from, not just the URL.