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Operations Research Analyst
Also called Optimisation Analyst
An Operations Research Analyst turns messy business decisions into optimisation models with explicit constraints. They help a company allocate scarce resources well and show how fragile or stable each answer is.
What they do
They take a decision such as scheduling, routing, assignment or inventory allocation and write it down as a model. That means agreeing the objective, listing the hard and soft constraints, and finding the data to feed it. They build the model with solvers and forecasting libraries, test it against real cases and compare it with what the business does today. They run sensitivity analysis to see which inputs move the answer. They then sit with operators and product owners to check the output is usable, and revise when a constraint turns out to be missing.
What a strong hire delivers
Within the first months, a strong hire delivers a working optimisation model for one live decision, with constraints agreed and written down. The business gets a sensitivity report showing which assumptions matter most. Forecast inputs are tested and documented. There is a clear comparison with the current method, and a plan for monitoring realised value once the model is in use.
How you can tell they are good
Ask them to walk through a model they built end to end. Listen for how they framed the objective, how they found missing constraints, and what happened when reality disagreed with the model. Ask how they would test feasibility and explain sensitivity to a non-specialist. Worry if they talk only about solver choice, or never mention realised results.
Why it is hard to fill
Titles vary widely. The same work appears as optimisation analyst, operations researcher, decision scientist or applied scientist. Many data scientists have forecasting skills but little combinatorial optimisation. Many classical operations researchers have not worked with modern data pipelines. Candidates who do both, and can talk to operators, are uncommon, so job titles are a poor filter.
Where to find them
Look in logistics, supply chain, energy, airlines, retail planning and marketplaces, where optimisation has long been part of daily operations. Strong candidates also come from operations research, applied mathematics and industrial engineering programmes, and from data science teams that own allocation or ranking problems. Consultancies with planning practices are another source.
Indicative pay
Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.
Interview questions for an Operations Research Analyst
- Describe an optimisation model you built that went into real use. What was the objective and what constraints shaped it?
A strong answer names a concrete decision, explains how the objective was chosen and how constraints were found, and says what changed in practice. Worry if the answer is all method and no outcome, or if constraints were simply handed over.
- How do you check that a model's solution is actually feasible in the real world, not just on paper?
Good answers cover validating constraints with operators, replaying historical cases, testing edge cases and checking for hidden rules. Worry if they trust the solver's feasibility status alone or never review output with the people who will act on it.
4 more questions, with what a strong answer covers, are in the Role Kit.
Job description you can post
We are looking for an Operations Research Analyst to help us make better decisions under real constraints. You will turn allocation, scheduling and ranking problems into optimisation models, test them against reality and work with the teams who depend on the results. We value clear thinking and evidence of models that worked.
What you will do
- Formulate business decisions as optimisation models with explicit constraints
- Build and maintain models using solvers and forecasting libraries
- Produce forecasts that feed decision models and test their quality
- Run sensitivity analysis and write clear reports on key assumptions
- Compare model output with current practice and track realised value
- Work with operators and product teams to find missing constraints
What you will bring
- Shipped at least one optimisation model that informed real decisions
- Strong grounding in probability, statistics and time-series modelling
- Hands-on experience with combinatorial optimisation and a solver toolkit
- Fluent in Python and the usual data science libraries
- Able to explain trade-offs and uncertainty to non-specialists
- Evidence of testing feasibility and measuring outcomes after launch
Nice to have
- Experience designing experiments to test decision changes
- Background in logistics, planning or marketplace problems
- Experience with ranking or recommendation under constraints
Free to use and adapt. If it saves you time, a link back to this page is appreciated.
Skills that matter
- Time-series modellingAdvancedModel temporal dependencies and forecasts
- Optimisation and operations researchAdvancedChoose actions subject to objectives and constraints
- Probability and statisticsWorkingQuantify variability and statistical evidence
- Experimental designWorkingChoose comparisons that identify credible effects
Other roles in forecasting, optimisation and recommendation
Related roles in other families
How we wrote this
Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.
- Data Scientists, 15-2051.00, O*NET / US Department of Labor
- Operations Research Analysts, US Bureau of Labor Statistics
- OR-Tools, Google
← Forecasting, optimisation and recommendationAll new job families
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Role Kit
Operations Research Analyst: the Role Kit
- What the role is and what a strong hire delivers
- Every interview question, with what a strong answer covers
- A ready-to-post job description
- Where to find the people, and the other titles they use
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