Interview scorecard template

Scientific Computing Specialist interview scorecard

Evaluate Scientific Computing Specialist 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 in Fortran, C++ or Python numerical stacks plus MPI, OpenMP and CUDA; ask about specific solvers, BLAS/LAPACK use, and floating point precision decisions. Use the rubric to compare role-specific evidence consistently.

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TL;DR
For technical proficiency, look for evidence the candidate names concrete kernels they optimised, discusses mixed precision and conditioning, and explains parallel decomposition choices without hiding behind library abstractions. For systems and trade-offs, look for evidence the candidate cites measured scaling curves, identifies the limiting resource (bandwidth, MPI collectives, filesystem), and justifies where they stopped optimising. 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 in Fortran, C++ or Python numerical stacks plus MPI, OpenMP and CUDA; ask about specific solvers, BLAS/LAPACK use, and floating point precision decisions.

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

Names concrete kernels they optimised, discusses mixed precision and conditioning, and explains parallel decomposition choices without hiding behind library abstractions.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe how they scaled a code across nodes on a cluster like Slurm-managed HPC: strong versus weak scaling, I/O bottlenecks, checkpointing, memory bandwidth limits.

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

Cites measured scaling curves, identifies the limiting resource (bandwidth, MPI collectives, filesystem), and justifies where they stopped optimising.

03
Evaluation factor

Evidence and rigour

25% weight

Test verification and validation habits: convergence studies, analytical benchmarks, regression tests for simulation output, profiling with tools such as VTune, Nsight or TAU.

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

Shows benchmarked results against known solutions, keeps reproducible run configs and version-pinned environments, and quantifies speedups rather than estimating them.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they support domain scientists: translating a physicist or biologist's model into performant code, training users, and documenting workflows on shared clusters.

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

Describes named collaborations where they refactored researcher code, left usable documentation, and negotiated scope against publication deadlines.

Evidence-led prompts

Interview questions for a Scientific Computing Specialist

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

  1. 01

    What types of scientific research projects have you supported?

  2. 02

    Can you describe a project where you applied computational skills to solve a problem?

  3. 03

    What is your background in scientific computing?

  4. 04

    Can you explain your experience with computational algorithms and their applications?

  5. 05

    How familiar are you with mathematical modelling and numerical optimisation?

See the complete Scientific Computing Specialist question set
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