Interview scorecard template

Computational Biology Specialist interview scorecard

Evaluate Computational Biology 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 with aligners and callers (BWA-MEM, STAR, GATK, DESeq2), plus scripting in Python, R and Bioconductor, and workflow managers such as Nextflow or Snakemake. Use the rubric to compare role-specific evidence consistently.

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software databioinformaticsgenomicsnextflowrna seq
TL;DR
For technical proficiency, look for evidence the candidate names specific versions, parameters and reference builds (GRCh38, T2T) and explains why each tool was chosen over alternatives. For systems and trade-offs, look for evidence the candidate describes concrete trade-offs on runtime, storage and cost, with containers, config profiles and pinned dependencies for reproducibility. 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 aligners and callers (BWA-MEM, STAR, GATK, DESeq2), plus scripting in Python, R and Bioconductor, and workflow managers such as Nextflow or Snakemake.

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 specific versions, parameters and reference builds (GRCh38, T2T) and explains why each tool was chosen over alternatives.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe how they handled scale: terabyte FASTQ batches, HPC schedulers like Slurm, cloud costs, containerised environments, and reproducible pipeline versioning across multi-cohort projects.

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 concrete trade-offs on runtime, storage and cost, with containers, config profiles and pinned dependencies for reproducibility.

03
Evaluation factor

Evidence and rigour

25% weight

Test statistical rigour: multiple testing correction, batch effect handling, power for differential expression, confounder control, and how they validated a computational finding experimentally.

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

Discusses FDR, covariate modelling and negative controls; cites a result that survived orthogonal validation or replication in an independent cohort.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they work with wet-lab scientists and clinicians: translating ambiguous biological questions, presenting figures, and documenting analyses in notebooks or Git repositories.

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

Gives examples of reshaping an experiment before sequencing started and delivering plots that non-computational colleagues acted on.

Evidence-led prompts

Interview questions for a Computational Biology Specialist

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

  1. 01

    Do you have experience designing algorithms for biological problems?

  2. 02

    How strong are your skills in programming languages such as Python or R?

  3. 03

    Do you have experience with computational tools for predicting protein function or structure?

  4. 04

    How would you handle large sets of biological data, and which tools would you use?

  5. 05

    How do you apply statistics and probability to biological systems?

See the complete Computational Biology Specialist question set
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