Interview scorecard template

Operations Research Intern Associate interview scorecard

Evaluate Operations Research Intern Associate candidates across 4 weighted areas: technical proficiency, systems and trade-offs, evidence and rigour, and collaboration and communication. Technical proficiency leads at 35%, so check fluency with LP, MIP and heuristic formulations: decision variables, constraints, objective functions, plus hands-on use of Gurobi, CPLEX, PuLP or OR-Tools in Python. Use the rubric to compare role-specific evidence consistently.

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software datagurobilinear programmingoptimizationsimulation modeling
TL;DR
For technical proficiency, look for evidence the candidate formulates a scheduling or routing problem aloud, names solver settings like MIP gap and time limits, and explains dual values correctly. For systems and trade-offs, look for evidence the candidate describes a model they built end to end, including data sources, runtime, solution quality, and the decision it actually informed. Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework

What to assess and how to score it

Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.

01
Evaluation factor

Technical proficiency

35% weight

Check fluency with LP, MIP and heuristic formulations: decision variables, constraints, objective functions, plus hands-on use of Gurobi, CPLEX, PuLP or OR-Tools in Python or R.

Evidence to listen for

  • Command of the languages, frameworks, and data tools the role actually uses
  • Understands correctness, performance, and failure modes, not just syntax
  • Has opinions on testing and can justify them
  • Reads and reasons about code they did not write

Five-point scoring guide

1
Poor

Cannot work independently; fundamentals are missing.

2
Needs Improvement

Weak fundamentals; output needs heavy review.

3
Satisfactory

Competent for the role; needs guidance on complex or unfamiliar work.

4
Very Good

Strong practitioner; handles hard problems with little guidance.

5
Excellent

Formulates a scheduling or routing problem aloud, names solver settings like MIP gap and time limits, and explains dual values correctly.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe coursework capstones, internships or competition entries: inventory policy models, vehicle routing, queueing or discrete-event simulations built in SimPy, Arena or AnyLogic with stated results.

Evidence to listen for

  • Reasons about scale, latency, cost, and failure before writing code
  • Names the trade-off they chose and what they gave up
  • Understands the data lifecycle end to end
  • Anticipates what breaks at ten times the volume

Five-point scoring guide

1
Poor

No thinking beyond the immediate task; no awareness of scale or failure.

2
Needs Improvement

Limited architectural awareness; struggles with design decisions.

3
Satisfactory

Works within an existing design; makes sound local decisions.

4
Very Good

Designs for scale and maintainability; articulates trade-offs clearly.

5
Excellent

Describes a model they built end to end, including data sources, runtime, solution quality, and the decision it actually informed.

03
Evaluation factor

Evidence and rigour

25% weight

Test how they validated a model: sensitivity analysis, baseline comparison, sanity-checking infeasibility, and whether assumptions about demand or capacity were tested against real data.

Evidence to listen for

  • Validates results rather than trusting output
  • Knows how their work is measured and what a bad result looks like
  • Can describe a time their own analysis or model was wrong and how they caught it
  • Careful about data quality, leakage, and silent failure

Five-point scoring guide

1
Poor

Ships unvalidated work; no notion of how correctness is checked.

2
Needs Improvement

Validates superficially; misses obvious quality or leakage issues.

3
Satisfactory

Reasonable checks in place; rigour drops under time pressure.

4
Very Good

Validates thoroughly; can name a real error they caught in their own work.

5
Excellent

Distinguishes model error from data error, runs scenarios before trusting output, and cites a case where results contradicted their assumption.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they translated model output for supply chain, logistics or finance stakeholders who do not read constraint matrices; look for dashboards, memos or recommendation decks.

Evidence to listen for

  • Explains technical work to non-technical stakeholders
  • Gives and takes code or peer review constructively
  • Documents enough that the work survives their absence
  • Aligns with team process rather than working around it

Five-point scoring guide

1
Poor

Cannot work in a team; resistant to feedback.

2
Needs Improvement

Communication issues create rework; lone-wolf tendencies.

3
Satisfactory

Adequate team member; documentation and review participation are light.

4
Very Good

Communicates well; reliable reviewer and collaborator.

5
Excellent

Explains a solver recommendation in plain operational terms, anticipates the pushback on feasibility, and documents assumptions where others can audit them.

Evidence-led prompts

Interview questions for a Operations Research Intern Associate

Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.

  1. 01

    Can you describe a project where you used operations research to solve a problem?

  2. 02

    What is your understanding of operations research?

  3. 03

    Can you describe using analysis to identify a problem and propose a solution?

  4. 04

    Do you have experience with optimisation methods?

  5. 05

    Which statistical methods are you most comfortable with?

See the complete Operations Research Intern Associate question set
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