Loading page content
Preparing content…
SOC 15-2031 · O*NET 15-2031.00
Use mathematical modeling, optimization, simulation, and data analysis to help organizations compare options under constraints such as cost, time, resources, and logistics, and explain assumptions and limitations to decision makers.
Model impact index for three equally weighted representative core tasks; not a measured share of working time or a job-loss probability. floor((50+75+75)/3+0.5)=67; fermatmind-ai-impact-v1; 2026-10-18.
If you are willing to turn complex operational problems into testable objectives, constraints, and data models and to explain the trade-offs of a plan, you can explore operations research analysis; fictional samples can be used to check the reproducible model, input data dictionary, scenario comparisons, and decision notes.
During review, check data quality, the objective function, constraints, and model robustness, and confirm responsibility and authorization boundaries.
Safe trial: Use fictional delivery demand to design two route or shift plans and write down the objective function, constraints, sensitivity, and failure conditions
Direct judgment: If you are willing to turn complex operational problems into testable objectives, constraints, and data models and to explain the trade-offs of a plan, you can explore operations research analysis
Use mathematical modeling, optimization, simulation, and data analysis to help organizations compare options under constraints such as cost, time, resources, and logistics, and explain assumptions and limitations to decision makers.
This page corresponds to O*NET 15-2031.00 Operations Research Analysts; the core is using mathematical modeling, optimization, simulation, and data analysis to help organizations compare options under constraints such as cost, time, resources, and logistics, and to explain assumptions and limitations to decision makers.
Career direction comparison
Management analysts lean toward organizational processes and management advice; look at the final output rather than similar titles.
| Career direction | Core work and output | Key differences | Best-fit choice |
|---|---|---|---|
| Data scientist | Broad data modeling and machine learning | Difference between data scientists and this occupation: operations research analysis focuses more on constraints and optimized decisions. | Choosing data science suits those who prefer exploring predictive models. |
| Statistician | Statistical design and inference | Operations research analysis focuses more on solving operational plans | Likes inference methods |
AI impact on this career
Tools can assist with code drafts, data cleaning suggestions, and plan summaries; objective and constraint setting, data quality judgment, and plan adoption responsibility must be verified by authorized personnel. AI Impact Score 67% is an editorial estimate. Model impact index for three equally weighted representative core tasks; not a measured share of working time or a job-loss probability. floor((50+75+75)/3+0.5)=67; fermatmind-ai-impact-v1; 2026-10-18.The three tasks, ratings and sources are listed in the FermatMind method source record on this page.
FermatMind AI Impact Score estimation method v1
FermatMind / 费马测试 · 2026-10-18
Editorial ratings use the existing occupational responsibilities and AI task analysis on this page. They are not adoption rates, measured effects, job-disappearance or personal-unemployment probabilities, or official scores from Stanford or another external institution. No measured time weights or future robot deployment are assumed.
Chinese mainland salary reference
Chinese positions require separate verification of region, employer, duties, shift system, and qualifications; the U.S. 15-2031.00 annual salary cannot be directly converted.
Insufficient evidence: China-wide wage percentiles and unified starting pay for this occupation are missing, and broad industry averages are not used as substitutes.
Insufficient evidence: a nationwide comparable median wage for Chinese operations research analysts was not obtained. In 2025, the pre-tax annual average for professional and technical personnel at enterprises above designated size was 155,491 yuan, which is only a broad position background for the direction of modeling and decision support tasks; it includes other professional positions, is not this occupation's wage or an individual range, government agencies and management approval positions require separate verification, and self-employed individuals and freelancers are excluded.
Insufficient to support precise wages for this occupation
U.S. wages
The U.S. 15-2031 had a 2025 median annual wage of 88940 U.S. dollars; overall growth of 11.9% and a net increase of 13500 over 2025—2035, with annual average openings of 7500 including replacements, not AI or individual unemployment.；The BLS precise occupation 15-2031.00 had a 2025 median annual wage of 88,940 U.S. dollars; statistical distributions do not represent individual contracts.
| Wage tier | Annual salary reference | Monthly equivalent | Explanation |
|---|---|---|---|
| Low wage percentile | $57,060 | Do not convert to Chinese monthly salary | Wage percentiles do not equal individual contracts or starting pay.；Wage percentiles should follow the official details for the same occupation; percentiles from parent groups or adjacent occupations must not be carried over. |
| Median | $88,940 | Do not convert to Chinese monthly salary |
Career fit guide
Can question data quality, assumptions, and boundaries, and explain optimization results to non-technical decision makers.
Problem definition： Confirm objectives, constraints, and evaluation metrics with stakeholders.
Data verification： Collect operational data and check for missing values, bias, and time definitions.
Model building： Build optimization, forecasting, or simulation models.
Work stress, risks, and occupational boundaries
Data bias: samples are outdated or missing.; May cause model output to mislead decisions. Control: perform quality checks and sensitivity analysis.
Omitted constraints: capacity, regulations, or manual rules are not counted.; May make a plan unexecutable. Control: have the executing party review the model.
Typical scenario：Optimizing a single cost while omitting service or fairness constraints.
Roles most affected：Analysts, objective setters, and teams affected by resource allocation. Objective context:；Overfitting the solution: the model works only in historical scenarios.; May fail when demand changes. Control: keep stress scenarios and alternatives.
Possible consequence：May sacrifice service or fairness objectives and make a plan unacceptable. Escalation boundary:；Shared escalation: when equipment, personnel, documentation, or authorization anomalies occur, pause first, document, and hand over to authorized personnel.
Getting started, qualifications, and career development
BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Career outlook and related occupation transitions
The U.S. precise occupation 15-2031 had 2025 employment of 113,100 people; overall growth of 11.9% and a net increase of 13,500 people over 2025—2035, not an AI attribution or individual layoffs.
United States · U.S. 15-2031.00
About 113,100 people
2025 employed
Employment and other background are interpreted separately:；Annual average openings of 7,500 over the same period include replacements, are not a net increase of 13,500 people or current hiring counts, and do not serve as AI adoption replacement or Chinese demand.；Actual opportunities vary by region, project, industry, and qualifications.；Automation may change some tasks, but BLS overall employment changes cannot be attributed to a single technology.；Adjacent occupations should be compared on responsibility for problem modeling and decision support, which is not the same as only producing reports in data analysis or directly making management approvals and its specific training.
15-2031 precise occupation employment stock; not current hiring, an AI attribution, or Chinese demand.
Use mathematical modeling, optimization, simulation, and data analysis to help organizations compare options under constraints such as cost, time, resources, and logistics, and explain assumptions and limitations to decision makers.
Confirm objectives, constraints, and evaluation metrics with stakeholders.; Collect operational data and check for missing values, bias, and time definitions.; Build optimization, forecasting, or simulation models.; Compare trade-offs among cost, time, resources, and service levels.
Choose a real task from this page for a small practice project or a conversation with someone in the field. Use the planning guides to set a date, an output and feedback questions before deciding whether to continue.
Positive evidence: Can question data quality, assumptions, and boundaries, and explain optimization results to non-technical decision makers.
Core process: Confirm objectives, constraints, and evaluation metrics with stakeholders.; Collect operational data and check for missing values, bias, and time definitions.; Build optimization, forecasting, or simulation models.; Compare trade-offs among cost, time, resources, and service levels.
Common tools and systems: spreadsheets, SQL, Python or R, optimization solvers, simulation and visualization tools.
U.S. occupational scope and Chinese position titles or credentials need not correspond one-to-one; local requirements are verified by region and employer.
| Dimension | Primary track | Comparison track |
|---|---|---|
| Problem definition | Confirm objectives, constraints, and evaluation metrics with stakeholders. | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
| Data verification | Collect operational data and check for missing values, bias, and time definitions. | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
| Model building | Build optimization, forecasting, or simulation models. | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
| Scenario solving | Compare trade-offs among cost, time, resources, and service levels. | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
| Sensitivity testing | Change demand or constraints and check plan robustness. | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
| Communicating results | Explain assumptions, limitations, and alternatives to decision makers. | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
| Deployment review | Track actual results and update the model and parameters. | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
Confirm objectives, constraints, and evaluation metrics with stakeholders.
Collect operational data and check for missing values, bias, and time definitions.
Build optimization, forecasting, or simulation models.
Compare trade-offs among cost, time, resources, and service levels.
Change demand or constraints and check plan robustness.
Explain assumptions, limitations, and alternatives to decision makers.
BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Verify hiring
Verify position
Verify feedback
Verify region
| Management analyst |
|---|
| Organizational processes and management advice |
| Does not necessarily build mathematical optimization models |
| Likes organizational consulting |
| Logistician | Supply chain operations and coordination | Operations research analysis can build logistics models but need not execute operations | Likes supply chain execution |
|---|
| Mathematician | Mathematical theory and applications | Operations research analysis addresses operational constraints more directly | Likes theoretical research |
|---|
| Industrial engineer | Production systems and process optimization | More often responsible for engineering implementation | Likes production floors |
|---|
Data scientists mainly do broad data modeling and machine learning; this occupation focuses more on problem modeling and decision support and is not the same as only producing reports in data analysis or directly making management approvals.
Compare final output, training, and responsibility before choosing a target occupation.
Tools can assist with part of the information organization; task descriptions do not equal automation rates.
Authorization, reliability, reviewers, and failure handling must be verified first.
15-2031 overall 11.9%, annual average 7500 including replacements, not a net increase of 13500; does not measure AI adoption replacement or individual layoffs.
| Work area | Task | Current change | Human control point |
|---|---|---|---|
| Problem definition | Confirm objectives, constraints, and evaluation metrics with stakeholders. | Assists with summarizing records and candidates | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
| Data verification | Collect operational data and check for missing values, bias, and time definitions. | Assists with summarizing records and candidates | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
| Model building | Build optimization, forecasting, or simulation models. | Assists with summarizing records and candidates | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
| Scenario solving | Compare trade-offs among cost, time, resources, and service levels. | Assists with summarizing records and candidates | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
| Sensitivity testing | Change demand or constraints and check plan robustness. | Assists with summarizing records and candidates | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
| Communicating results | Explain assumptions, limitations, and alternatives to decision makers. | Assists with summarizing records and candidates | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
| Deployment review | Track actual results and update the model and parameters. | Assists with summarizing records and candidates | Objective and constraint setting, data quality judgment, and plan adoption responsibility |
O*NET lists 15-2031.00 tasks; BLS employment tables do not measure single-item AI effects.
Code, data cleaning, and summary assistance are editorial trial suggestions; objectives, constraints, data quality, and plan adoption require human verification, and no single-item AI effect measurements were obtained.
AI primarily changesAssists with summarizing records and candidates
Human responsibility remainsObjective and constraint setting, data quality judgment, and plan adoption responsibility
AI primarily changesAssists with summarizing records and candidates
Human responsibility remainsObjective and constraint setting, data quality judgment, and plan adoption responsibility
AI primarily changesAssists with summarizing records and candidates
Human responsibility remainsObjective and constraint setting, data quality judgment, and plan adoption responsibility
Use fictional samples only when trying tools; real operations require verifying data permissions, model validation, reproducibility, and fairness.
Confirm objectives, constraints, and evaluation metrics with stakeholders.
Collect operational data and check for missing values, bias, and time definitions.
Reproducible model, input data dictionary, scenario comparisons, and decision notes
Review objectives and limits with the business side.
Objective and constraint setting, data quality judgment, and plan adoption responsibility
The plan is unacceptable in reality.
Control: Review objectives and limits with the business side.
Model output misleads decisions.
Control: Perform quality checks and sensitivity analysis.
The plan is unexecutable.
Control: Have the executing party review the model.
It fails when demand changes.
Control: Keep stress scenarios and alternatives.
Managers misuse the results.
Control: Document assumptions and marginal effects.
Use fictional samples to learn about problem definition.
Check the reproducible model, input data dictionary, scenario comparisons, and decision notes, and observe authorization.
Check objective and constraint setting, data quality judgment, and plan adoption responsibility and anomaly escalation.
Complete training for the target equipment or position and verify qualifications.
No; objective and constraint setting, data quality judgment, and plan adoption responsibility must be verified by humans.
Source: Official occupational informationOriginal source ↗
Return to the original records and pause high-risk operations.
Source: Official occupational informationOriginal source ↗
Use only approved systems and permissions.
Source: Official occupational informationOriginal source ↗
Cross-check against the reproducible model, input data dictionary, scenario comparisons, decision notes, and the site.
Source: Official occupational informationOriginal source ↗
Official employment tables cannot be used to derive single-item replacement rates.
Source: Official occupational informationOriginal source ↗
Objective and constraint setting, data quality judgment, and plan adoption responsibility
Source: Official occupational informationOriginal source ↗
Review objectives and limits with the business side.
Source: Official occupational informationOriginal source ↗
Moving to an adjacent position requires training and qualifications for the target occupation.
Must not be replaced with figures for other occupations or generic positions
Chinese positions do not map one-to-one to U.S. 15-2031.00.
| Education level | Position direction | Explanation |
|---|---|---|
| Bachelor's degree foundation | Mathematics and statistics | Check position requirements. |
| Programming training | Python, SQL, and optimization | Build reproducible samples. |
| Industry practice | Business constraints and data | Verify with the executing party. |
| Advanced study | Simulation and advanced optimization | Per responsibility. |
Check region, employer, qualifications, hours, and benefits.
Routes, inventory, and scheduling.
Capacity and production planning.
Resource and service allocation.
Verification note: local position requirements and authority over data, models, and plan adoption require separate verification of employer and local rules; NBS wage statistics do not prove licensing or organizational approval authority.
| Wage percentiles do not equal individual contracts or starting pay.；Region, industry, shift system, hours, experience, and qualifications can change actual offers. |
| High wage percentile | $159,910 | Do not convert to Chinese monthly salary | Wage percentiles do not equal individual contracts or starting pay.；In 2025, about 113100 people were employed in the United States, with the statistical scope being 15-2031.00. |
|---|
Salary 10th percentile：$57,060；2025 U.S. 15-2031 precise wage percentiles, not starting pay or individual offers.
Salary 25th percentile：$68,360；2025 U.S. 15-2031 precise wage percentiles, not starting pay or individual offers.
Median salary：$88,940；2025 U.S. 15-2031 precise wage percentiles, not starting pay or individual offers.
Salary 75th percentile：$125,990；2025 U.S. 15-2031 precise wage percentiles, not starting pay or individual offers.
Salary 90th percentile：$159,910；2025 U.S. 15-2031 precise wage percentiles, not starting pay or individual offers.
No; the market and position bases differ.；The U.S. precise occupation 15-2031 is projected to grow 11.9% overall over 2025—2035, with an official net increase of 13,500 people and annual average openings of 7,500 including replacements; openings are not net change or current hiring, and projections are not AI adoption replacement or individual unemployment.
No, it is the midpoint of the wage distribution.；U.S. wages and projections cannot replace Chinese wages or individual hiring probability.
No; annual average openings of 7500 include replacements and are not a ten-year net increase of 13500.
Overall projections cannot be used to derive single-technology causation.
| Industry | Median annual wage (U.S. dollars) | Interpretation boundaries |
|---|---|---|
| Professional, scientific, and technical services | $94,990 | BLS OOH: May 2025 median annual wage for U.S. 15-2031 in that industry; not industry percentiles, starting pay, or Chinese wages. |
| Federal government | $139,980 | BLS OOH: May 2025 median annual wage for U.S. 15-2031 in that industry; not industry percentiles, starting pay, or Chinese wages. |
| Finance and insurance | $80,870 | BLS OOH: May 2025 median annual wage for U.S. 15-2031 in that industry; not industry percentiles, starting pay, or Chinese wages. |
| Manufacturing | $108,120 | BLS OOH: May 2025 median annual wage for U.S. 15-2031 in that industry; not industry percentiles, starting pay, or Chinese wages. |
Problem scale and responsibility affect offers.
Organizational budgets and markets differ.
Both solving and implementation ability matter.
BLS 15-2031.00 precise occupation.
Overall growth of 11.9% and official net increase of 13500, not an AI attribution.
Annual average 7500 includes replacements, not a net increase of 13500 or current hiring.
Can question data quality, assumptions, and boundaries, and explain optimization results to non-technical decision makers.
What abilities and ways of working are needed to be an operations research analyst?
Ability judgment can observe supervised modeling, data verification, and decision-note feedback; interests are for exploration only and do not prove model quality or approval authority.
Do not use it this way: Interest assessments do not prove ability or qualification for objective and constraint setting, data quality judgment, or plan adoption responsibility.
View the Holland interest assessmentWhat abilities and ways of working are needed to be an operations research analyst?
Check the data dictionary, units, missing values, time windows, and model reproducibility, and record errors found.
Do not use it this way: Interest assessments do not prove ability or qualification for objective and constraint setting, data quality judgment, or plan adoption responsibility.
View the Holland interest assessmentWhat abilities and ways of working are needed to be an operations research analyst?
Practice identifying sample bias, infeasible constraints, and scenario failure, and explain pause and escalation conditions.
Do not use it this way: Interest assessments do not prove ability or qualification for objective and constraint setting, data quality judgment, or plan adoption responsibility.
View the Holland interest assessmentWhat abilities and ways of working are needed to be an operations research analyst?
Compare whether quantitative solving, business collaboration, and explaining trade-offs fit your work values.
Do not use it this way: Interest assessments do not prove ability or qualification for objective and constraint setting, data quality judgment, or plan adoption responsibility.
View the Holland interest assessmentWhat abilities and ways of working are needed to be an operations research analyst?
Check whether office modeling, business interviews, cross-team collaboration, and deadlines suit you.
Do not use it this way: Interest assessments do not prove ability or qualification for objective and constraint setting, data quality judgment, or plan adoption responsibility.
View the Holland interest assessmentWhat abilities and ways of working are needed to be an operations research analyst?
Use the existing fictional delivery exercise to explain objectives, constraints, and sensitivity; samples do not grant real scheduling authority.
Do not use it this way: Interest assessments do not prove ability or qualification for objective and constraint setting, data quality judgment, or plan adoption responsibility.
View the Holland interest assessmentFits better when: Likes exploring predictive models
Watch for: Target occupation education, qualifications, and responsibility scope require separate verification.
Explore related career: Data scientistFits better when: Likes inference methods
Watch for: Target occupation education, qualifications, and responsibility scope require separate verification.
Explore related career: StatisticianFits better when: Likes organizational consulting
Watch for: Target occupation education, qualifications, and responsibility scope require separate verification.
Explore related career: Management analystFits better when: Likes supply chain execution
Watch for: Target occupation education, qualifications, and responsibility scope require separate verification.
Explore related career: LogisticianFits better when: Likes theoretical research
Watch for: Target occupation education, qualifications, and responsibility scope require separate verification.
Explore related career: MathematicianFits better when: Likes production floors
Watch for: Target occupation education, qualifications, and responsibility scope require separate verification.
Explore related career: Industrial engineerVerify with fictional samples and supervisory feedback; real work depends on qualification authorization.
Verify with fictional samples and supervisory feedback; real work depends on qualification authorization.
Verify with fictional samples and supervisory feedback; real work depends on qualification authorization.
Verify with fictional samples and supervisory feedback; real work depends on qualification authorization.
Verify with fictional samples and supervisory feedback; real work depends on qualification authorization.
Verify with fictional samples and supervisory feedback; real work depends on qualification authorization.
Verify with fictional samples and supervisory feedback; real work depends on qualification authorization.
Verify with fictional samples and supervisory feedback; real work depends on qualification authorization.
Verify with fictional samples and supervisory feedback; real work depends on qualification authorization.
Verify with fictional samples and supervisory feedback; real work depends on qualification authorization.
How to reduce risk：Review objectives and limits with the business side.
Evidence：Occupational activities · Occupational activities
Typical scenario：Samples are outdated or missing.
Roles most affected：Analysts, data providers, and decision makers using the model.
Possible consequence：May make estimates and plans diverge from real demand.
How to reduce risk：Perform quality checks and sensitivity analysis.
Evidence：Occupational activities · Occupational activities
Typical scenario：Capacity, regulations, or manual rules are not counted.
Roles most affected：Analysts, business executing parties, and decision makers.
Possible consequence：May produce an unexecutable plan that violates capacity or business conditions.
How to reduce risk：Have the executing party review the model.
Evidence：Occupational activities · Occupational activities
Typical scenario：The model works only in historical scenarios.
Roles most affected：Model developers, operations teams, and plan users.
Possible consequence：May fail when demand changes and lead to misallocated resources.
How to reduce risk：Keep stress scenarios and alternatives.
Evidence：Occupational activities · Occupational activities
Typical scenario：Cannot explain why the plan changed.
Roles most affected：Analysts, managers, and teams executing the plan.
Possible consequence：May misunderstand model conditions or use results for out-of-scope decisions.
How to reduce risk：Document assumptions and marginal effects.
Evidence：Occupational activities · Occupational activities
Typical scenario：Client or operational details are uploaded.
Roles most affected：Analysts, clients, data subjects, and data administrators.
Possible consequence：May expose restricted details and violate applicable contracts or data rules.
How to reduce risk：Use approved environments and de-identified samples.
Evidence：Occupational activities · Occupational activities
| Stage | Initial tasks | Boundary | Evidence at work | Possible check |
|---|---|---|---|---|
| Problem definition | Confirm objectives, constraints, and evaluation metrics with stakeholders. | Objective and constraint setting, data quality judgment, and plan adoption responsibility | Reproducible model, input data dictionary, scenario comparisons, and decision notes | Verify problem definition records |
| Data verification | Collect operational data and check for missing values, bias, and time definitions. | Objective and constraint setting, data quality judgment, and plan adoption responsibility | Reproducible model, input data dictionary, scenario comparisons, and decision notes | Verify data verification records |
| Model building | Build optimization, forecasting, or simulation models. | Objective and constraint setting, data quality judgment, and plan adoption responsibility | Reproducible model, input data dictionary, scenario comparisons, and decision notes | Verify model building records |
| Scenario solving | Compare trade-offs among cost, time, resources, and service levels. | Objective and constraint setting, data quality judgment, and plan adoption responsibility | Reproducible model, input data dictionary, scenario comparisons, and decision notes | Verify scenario solving records |
| Sensitivity testing | Change demand or constraints and check plan robustness. | Objective and constraint setting, data quality judgment, and plan adoption responsibility | Reproducible model, input data dictionary, scenario comparisons, and decision notes | Verify sensitivity testing records |
Confirm objectives, constraints, and evaluation metrics with stakeholders.
Collect operational data and check for missing values, bias, and time definitions.
Build optimization, forecasting, or simulation models.
Record the 20 items/integer full shifts/capacity and cost and the three candidates, compare only the three given cases, and do not assume real delivery rules.
Data dictionary checks A(12,100), B(10,90), at most 2 shifts each, and full-shift pricing with partial loading allowed; do not mix in wage statistics.
List capacity and cost for the three cases; with 20 items choose 2B at 180; with 23 items only 2A at 200 works; attach failure conditions and model limitations.
| Stage | Evidence at work | Boundary | Possible check |
|---|---|---|---|
| Objective mis-specification | Optimizing a single cost while omitting service or fairness constraints. | Review objectives and limits with the business side. | Verify objective mis-specification handling |
| Data bias | Samples are outdated or missing. | Perform quality checks and sensitivity analysis. | Verify data bias handling |
| Omitted constraints | Capacity, regulations, or manual rules are not counted. | Have the executing party review the model. | Verify omitted constraint handling |
| Overfitting the solution | The model works only in historical scenarios. | Keep stress scenarios and alternatives. | Verify overfitting handling |
Copy the problem statement parameters, full-shift costs, and objectives and constraints; do not adopt real client routes.
Compute A+B/2A/2B capacity, cost, and feasibility by hand or spreadsheet; with 20 items the cheapest is 180; keep the calculations and data dictionary.
Re-computing with demand 23 shows only 2A is feasible at 200; write up omitted assumptions/sensitivity/decision notes, submit a reproducible table, and do not execute real scheduling.
| Stage | Evidence at work | Credential or status | Exam or registration conditions | Important limitation | Best suited to | How to demonstrate it |
|---|---|---|---|---|---|---|
| Job observation | Reproducible model, input data dictionary, scenario comparisons, and decision notes | BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree. | By region and employer | Training does not automatically grant independent authority | Learner | Verify courses, authorization, and feedback |
| Basic training | Reproducible model, input data dictionary, scenario comparisons, and decision notes | BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree. | By region and employer | Training does not automatically grant independent authority | Learner | Verify courses, authorization, and feedback |
| Supervised practice | Reproducible model, input data dictionary, scenario comparisons, and decision notes | BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree. | By region and employer | Training does not automatically grant independent authority | Learner | Verify courses, authorization, and feedback |
| Qualification verification | Reproducible model, input data dictionary, scenario comparisons, and decision notes | BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree. | By region and employer | Training does not automatically grant independent authority | Practitioner | Verify courses, authorization, and feedback |
| Independent handoff | Reproducible model, input data dictionary, scenario comparisons, and decision notes | BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree. | By region and employer | Training does not automatically grant independent authority | Practitioner | Verify courses, authorization, and feedback |
| Advanced review | Reproducible model, input data dictionary, scenario comparisons, and decision notes | BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree. | By region and employer | Training does not automatically grant independent authority | Practitioner | Verify courses, authorization, and feedback |
Typical scope：Confirm objectives, constraints, and evaluation metrics with stakeholders.; As authorized by the position.
Readiness evidence：Reproducible model, input data dictionary, scenario comparisons, and decision notes
Credential boundary：BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Next moves：Check quality and authority first, then expand task scope.
Typical scope：Confirm objectives, constraints, and evaluation metrics with stakeholders.; As authorized by the position.
Readiness evidence：Reproducible model, input data dictionary, scenario comparisons, and decision notes
Credential boundary：BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Next moves：Check quality and authority first, then expand task scope.
Typical scope：Confirm objectives, constraints, and evaluation metrics with stakeholders.; As authorized by the position.
Readiness evidence：Reproducible model, input data dictionary, scenario comparisons, and decision notes
Credential boundary：BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Next moves：Check quality and authority first, then expand task scope.
Typical scope：Confirm objectives, constraints, and evaluation metrics with stakeholders.; As authorized by the position.
Readiness evidence：Reproducible model, input data dictionary, scenario comparisons, and decision notes
Credential boundary：BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Next moves：Check quality and authority first, then expand task scope.
Typical scope：Build optimization, forecasting, or simulation models.; As authorized by the position.
Readiness evidence：Reproducible model, input data dictionary, scenario comparisons, and decision notes
Credential boundary：BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Next moves：Check quality and authority first, then expand task scope.
Typical scope：Build optimization, forecasting, or simulation models.; As authorized by the position.
Readiness evidence：Reproducible model, input data dictionary, scenario comparisons, and decision notes
Credential boundary：BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Next moves：Check quality and authority first, then expand task scope.
Typical scope：Build optimization, forecasting, or simulation models.; As authorized by the position.
Reproducible model, input data dictionary, scenario comparisons, and decision notes
Typical scope：Change demand or constraints and check plan robustness.; As authorized by the position.
Readiness evidence：Reproducible model, input data dictionary, scenario comparisons, and decision notes
Credential boundary：BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Next moves：Check quality and authority first, then expand task scope.
Typical scope：Change demand or constraints and check plan robustness.; As authorized by the position.
Readiness evidence：Reproducible model, input data dictionary, scenario comparisons, and decision notes
Credential boundary：BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Next moves：Check quality and authority first, then expand task scope.
Typical scope：Change demand or constraints and check plan robustness.; As authorized by the position.
Reproducible model, input data dictionary, scenario comparisons, and decision notes
BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.; Real duties depend on local rules and employer authorization, and changing roles requires training for the target position.
Collect operational data and check for missing values, bias, and time definitions.
Reproducible model, input data dictionary, scenario comparisons, and decision notes
Objective and constraint setting, data quality judgment, and plan adoption responsibility
U.S. 15-2031 precise occupation · BLS precise occupation
11.9%
2025—2035 growth
Overall growth of 11.9% and an official net increase of 13500 people.
Does not establish：Not AI adoption replacement, individual layoffs, or Chinese demand.
U.S. 15-2031 precise occupation · Projections include replacements
About 7,500
Annual average openings
Annual average 7500 includes replacements, not a net increase of 13500 or current hiring.
Does not establish：Not AI adoption replacement, individual layoffs, or Chinese demand.
These are capability relationships, not automatic promotions or credential waivers.
Transfers：Mathematical modeling, statistical inference, programming, data quality assessment, and communication
Build next：Operations research analysis focuses more on constraints and optimized decisions
View career profileTransfers：Mathematical modeling, statistical inference, programming, data quality assessment, and communication
Build next：Operations research analysis focuses more on solving operational plans
View career profileTransfers：Mathematical modeling, statistical inference, programming, data quality assessment, and communication
Build next：Does not necessarily build mathematical optimization models
View career profileTransfers：Mathematical modeling, statistical inference, programming, data quality assessment, and communication
Build next：Operations research analysis can build logistics models but need not execute operations
View career profileTransfers：Mathematical modeling, statistical inference, programming, data quality assessment, and communication
Build next：Operations research analysis addresses operational constraints more directly
View career profileTransfers：Mathematical modeling, statistical inference, programming, data quality assessment, and communication
Build next：More often responsible for engineering implementation
View career profileTransfers：Mathematical modeling, statistical inference, programming, data quality assessment, and communication
Build next：Operations research analysis focuses more on constraints and optimized decisions
View career profileTransfers：Mathematical modeling, statistical inference, programming, data quality assessment, and communication
Build next：Operations research analysis focuses more on solving operational plans
View career profileBLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Objective mis-specification: optimizing a single cost while omitting service or fairness constraints.; Review objectives and limits with the business side.
No; objective and constraint setting, data quality judgment, and plan adoption responsibility are verified by authorized personnel.
Insufficient evidence: nationwide medians, percentiles, and starting pay for this occupation were not obtained. In 2025, the pre-tax annual average for the broad professional and technical position at enterprises above designated size was 155491 yuan, covering only the modeling and professional decision support direction, including other professional positions and not operations research wages or an individual range; the 172877 figure for the same population at scientific research and technical service enterprises covers only that industry's employer subgroup; government agencies require separate verification, and self-employed freelancers are excluded.
The U.S. 15-2031 had a 2025 median annual wage of 88940 U.S. dollars; overall growth of 11.9% and a net increase of 13500 people over 2025—2035, with annual average openings of 7500 including replacements, not current hiring, AI adoption replacement, or individual layoffs.
Use fictional delivery demand to design two route or shift plans and write down the objective function, constraints, sensitivity, and failure conditions
The U.S. 15-2031.00 precise occupation is kept separate from Chinese positions; wages and projections retain the year and statistical scope.
2026-10-12
BLS U.S. 15-2031 wages and employment; O*NET tasks and wage percentiles; NBS2025 broad professional and technical position and the scientific research and technical services enterprise subgroup.
Limitations：Certification is an optional ability signal and does not equal a practice license or a hiring guarantee.
Limitations：U.S. occupational scope.
Limitations：Mention rates in job ads are not ability importance or a hiring guarantee.
Limitations：Global exposure research, not a layoff probability projection for this occupation.
Limitations：Includes other professional positions and is not an occupational median, percentile, starting pay, or individual range; the research subgroup is not the population of all industries and units; self-employed freelancers are excluded; agencies require separate verification; and it does not prove licensing.
Limitations：A voluntary cross-industry framework that does not replace local law or organizational authorization.
Limitations：U.S. occupational scope; employment data comes from BLS.
Limitations：Shows data freshness only and does not by itself prove market causation.
Limitations：U.S. precise salaried wage distribution, not starting pay, individual offers, or Chinese wages.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：O*NET OnLine presents BLS OEWS wage data; preserve the displayed period, worker population, geography and unit. A direct BLS access failure does not invalidate this opened O*NET page.
Limitations：Use only for the stated O*NET occupation, US market and page subject. It does not establish a China mapping, China pay, or legal authorization.
Limitations：Openings are not net change or current hiring, not AI adoption replacement, individual layoffs, or Chinese demand, and do not support wage percentiles.
Limitations：Editorial ratings use the existing occupational responsibilities and AI task analysis on this page. They are not adoption rates, measured effects, job-disappearance or personal-unemployment probabilities, or official scores from Stanford or another external institution. No measured time weights or future robot deployment are assumed.
The evidence supports only scope-limited judgments about operations research analysts in the labeled market and period, and does not constitute a guarantee of personal income, hiring, or fit.
MBTI assessment (English) · Big Five personality assessment (English) · MBTI assessment (Chinese) · Big Five personality assessment (Chinese)
Verify validity period
Verify local rules
Credential boundary：BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Next moves：Check quality and authority first, then expand task scope.
Typical scope：Build optimization, forecasting, or simulation models.; As authorized by the position.
Readiness evidence：Reproducible model, input data dictionary, scenario comparisons, and decision notes
Credential boundary：BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Next moves：Check quality and authority first, then expand task scope.
Credential boundary：BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Next moves：Check quality and authority first, then expand task scope.
Typical scope：Change demand or constraints and check plan robustness.; As authorized by the position.
Readiness evidence：Reproducible model, input data dictionary, scenario comparisons, and decision notes
Credential boundary：BLS lists that a bachelor's degree is usually required; training in mathematics, statistics, computer science, engineering, or a related field helps, and some positions prefer a graduate degree.
Next moves：Check quality and authority first, then expand task scope.
