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Operations Research Analysts

SOC 15-2031.00Job Zone 5 · Extensive Preparationv.26.05

Context coveredThis framework covers operations research practice in enterprise, government, and consulting environments where advanced quantitative modeling, data analysis, and evidence-based decision support are applied to complex operational and strategic problems.

Sources:O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.SkillsCrosswalkSkillsCrosswalk — O*NET enrichment + adjacency index. Opens in new tab.
Emerging
Entry / Apprentice
  1. Mathematical and simulation model components — identify and document under direct supervision when formulating initial problem representations in a structured analytical environment.
  2. Data requirements for assigned analysis tasks — gather and organize using established protocols and statistical validation procedures within a team-based operations research project.
  3. Analytical or scientific software tools — apply to run predefined model configurations and record outputs under guidance from senior analysts in a professional analytics setting.
  4. Operational problems described by management — interpret and restate as structured problem definitions with support from experienced colleagues in a consulting or corporate OR team.
  5. Model validation procedures — execute using standard testing scripts and report discrepancies to supervising analysts during the model development lifecycle.
  6. Management reports summarizing analytical findings — draft initial sections following established organizational templates under close review by senior staff.
  7. Current operational systems under study — observe and record component behaviors and data flows using structured observation checklists in manufacturing, logistics, or service environments.
  8. Database query tools and management software — use to extract and stage relevant datasets for analysis under direction in a data-rich enterprise environment.
  9. Quantitative findings from completed analyses — present in structured formats to internal team members under rehearsal conditions supervised by a senior analyst.
  10. Active listening and reading comprehension skills — apply to absorb technical briefings and stakeholder inputs accurately during problem scoping sessions with organizational clients.
Developing
Mid-level / Established
  1. Mathematical or simulation models of operational problems — formulate independently by defining variables, constraints, and objective functions for moderately complex scenarios in logistics, finance, or operations settings.
  2. Data validation and statistical testing procedures — design and execute with limited oversight to confirm dataset integrity before model calibration in a professional OR environment.
  3. Model adequacy assessments — conduct using sensitivity analysis and scenario testing, reformulating model structures when performance benchmarks are not met on assigned projects.
  4. Management-facing analytical reports — prepare with clear problem definitions, methodology summaries, and actionable recommendations for recurring operational challenges.
  5. Cross-functional project teams — collaborate with to align analytical outputs with implementation constraints across engineering, IT, and operations departments.
  6. Analytical software platforms such as simulation and optimization suites — configure and adapt for project-specific requirements in a mid-size corporate or government analytical unit.
  7. Operational system observations and multi-source data collection — synthesize into coherent component-level problem analyses supporting decision-making for supply chain or resource allocation problems.
  8. Results of quantitative modeling and data analysis — present to management audiences using structured visualizations and plain-language narratives in stakeholder briefings.
  9. Complex problem-solving frameworks — apply adaptively when standard solution approaches are insufficient, drawing on cross-disciplinary knowledge in production, engineering, or technology domains.
  10. Time management and project coordination skills — exercise to deliver phased analytical deliverables on schedule within multi-analyst OR engagements subject to organizational deadlines.
Proficient
Senior / Expert IC
  1. Large-scale mathematical and simulation models — formulate autonomously for high-complexity, multi-variable operational problems spanning conflicting objectives and binding real-world constraints in enterprise or government contexts.
  2. Full data requirements lifecycle — define, validate, and govern end-to-end using advanced statistical tests and judgment-based quality controls for mission-critical analytical programs.
  3. Model validation and reformulation cycles — lead across the complete development pipeline, applying rigorous adequacy testing and iterative redesign to ensure solution reliability in production deployments.
  4. Comprehensive management reports on complex operational problems — author independently, synthesizing quantitative evidence with strategic recommendations targeted to executive decision-makers.
  5. Implementation of chosen analytical solutions — champion and facilitate across organizational boundaries, resolving technical and stakeholder obstacles through skilled coordination and systems analysis.
  6. Operational system components and interdependencies — analyze holistically using diverse data sources and advanced systems evaluation techniques to uncover root causes of performance deficiencies.
  7. Non-routine analytical challenges involving novel data types or emergent problem structures — resolve by applying inductive and deductive reasoning with advanced mathematical and computational methods.
  8. High-stakes presentation of modeling results and analytical conclusions — deliver persuasively to senior leadership and external clients, adapting technical depth to audience expertise.
  9. Advanced analytical and scientific software ecosystems including optimization, simulation, and statistical platforms — integrate and customize to meet complex, project-specific modeling requirements.
  10. Judgment and decision-making under uncertainty — exercise with organizational consequence, selecting among competing analytical approaches based on risk tolerance, data quality, and strategic priorities.
Advanced
Lead / Principal / Executive
  1. Organizational operations research strategy and methodological standards — define and institutionalize to ensure analytical rigor and strategic alignment across all OR programs and teams.
  2. Enterprise-wide problem conceptualization frameworks — develop and champion to translate ambiguous organizational challenges into well-posed mathematical models at portfolio scale.
  3. Next-generation modeling and analytical capabilities — pioneer by integrating emerging computational methods, machine learning, and simulation paradigms into the organization's analytical infrastructure.
  4. Senior and junior operations research professionals — mentor and develop through structured learning strategies, code and model reviews, and progressive assignment of high-complexity problem ownership.
  5. Cross-enterprise implementation of transformational analytical solutions — lead by aligning executive sponsors, functional leaders, and technical teams to overcome adoption barriers at organizational scale.
  6. Analytical governance policies and data quality standards — establish and enforce across departments to ensure defensible, reproducible operations research outputs used in high-stakes decisions.
  7. Organizational leadership and C-suite stakeholders — advise authoritatively on complex operational and strategic decisions by translating advanced quantitative findings into clear executive guidance.
  8. Research partnerships with academic institutions, government agencies, and industry consortia — cultivate and direct to advance the organization's OR capabilities and influence field-level best practices.
  9. Investment prioritization for analytical technology platforms and OR talent pipelines — lead by evaluating emerging tools, assessing organizational capability gaps, and allocating resources strategically.
  10. Culture of intellectual curiosity, innovation, and analytical rigor — foster organization-wide by modeling achievement orientation, sponsoring experimental initiatives, and recognizing high-impact analytical contributions.

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Sources:Anthropic Economic IndexAnthropic Economic Index — release_2026_03_24. Opens in new tab.Jadhav & Danve, 2026Skill Automation Feasibility Index — Jadhav & Danve, 2026 (arXiv:2604.06906). Opens in new tab.WEF Skills TaxonomyWEF Skills Taxonomy 2021 — Building a Common Language for Skills at Work. Opens in new tab.

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Sources:Pathsmith™ Durable SkillsPathsmith™ Durable Skills Framework — America Succeeds + CompTIA. Opens in new tab.O*NET v30.2O*NET Resource Center — Occupational Information Network, v30.2 (Sept 2025). Opens in new tab.
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Source anchors that ground each statement

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RAPIDS apprenticeships
—
O*NET skills
MathematicsComplex Problem SolvingActive ListeningWritingSpeakingCritical ThinkingReading ComprehensionActive LearningJudgment and Decision MakingSystems AnalysisSystems EvaluationOperations AnalysisScienceTime ManagementCoordinationLearning StrategiesInstructing
Knowledge domains
MathematicsComputers and ElectronicsEngineering and TechnologyProduction and ProcessingEnglish LanguageDesignEducation and Training
Abilities
Mathematical ReasoningDeductive ReasoningInductive ReasoningWritten ExpressionOral ExpressionWritten ComprehensionNumber FacilityProblem SensitivityOral ComprehensionInformation Ordering
Work styles
Intellectual CuriosityAttention to DetailAchievement OrientationDependabilityInnovationCautiousness
Technology
Analytical or scientific softwareData base user interface and query softwareData base management system softwareOperating system softwareCustomer relationship management CRM softwareFinancial analysis softwareDevelopment environment softwareObject or component oriented development softwareAccess softwareComputer aided design CAD software
Tasks · seed anchors for statements
  1. Present the results of mathematical modeling and data analysis to management or other end users.
  2. Define data requirements, and gather and validate information, applying judgment and statistical tests.
  3. Perform validation and testing of models to ensure adequacy, and reformulate models, as necessary.
  4. Prepare management reports defining and evaluating problems and recommending solutions.
  5. Collaborate with others in the organization to ensure successful implementation of chosen problem solutions.
  6. Formulate mathematical or simulation models of problems, relating constants and variables, restrictions, alternatives, conflicting objectives, and their numerical parameters.
  7. Observe the current system in operation, and gather and analyze information about each of the component problems, using a variety of sources.
  8. Analyze information obtained from management to conceptualize and define operational problems.
CIP education codes
14.370152.1301

Sources: O*NET v30.2 (CC BY 4.0), SkillsCrosswalk.com, LER.me®, Anthropic Economic Index, SAFI (Jadhav & Danve, 2026), WEF Skills Taxonomy 2021, Pathsmith™ Durable Skills Framework. © 2026 EBSCOed.