Interview scorecard template

Bioinformatics Specialist interview scorecard

Evaluate Bioinformatics 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 hands-on command of alignment and variant tools (BWA-MEM, STAR, GATK, samtools/bcftools) plus Python or R, and workflow managers like Nextflow, Snakemake or WDL. Use the rubric to compare role-specific evidence consistently.

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software databioinformaticsngs pipelinesrna seqvariant calling
TL;DR
For technical proficiency, look for evidence the candidate names specific tool versions and parameters, explains why they chose them, and shows pipeline code or repos they wrote themselves. For systems and trade-offs, look for evidence the candidate quantifies compute and storage decisions, and articulates concrete trade-offs such as downsampling or aligner choice against recall on low-frequency variants. 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 hands-on command of alignment and variant tools (BWA-MEM, STAR, GATK, samtools/bcftools) plus Python or R, and workflow managers like Nextflow, Snakemake or WDL.

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 tool versions and parameters, explains why they chose them, and shows pipeline code or repos they wrote themselves.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe how they handled scale: terabyte FASTQ volumes, cluster or cloud scheduling (Slurm, AWS Batch), container reproducibility, and choices between speed, cost and sensitivity.

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

Quantifies compute and storage decisions, and articulates concrete trade-offs such as downsampling or aligner choice against recall on low-frequency variants.

03
Evaluation factor

Evidence and rigour

25% weight

Test statistical rigour on differential expression, multiple testing correction, batch effects, and how they validated calls against truth sets like GIAB or orthogonal assays.

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 thresholds, confounder adjustment and benchmarking metrics (precision, recall, F1) with results from real datasets they analysed.

04
Evaluation factor

Collaboration and communication

15% weight

Assess how they work with wet-lab scientists and clinicians: translating experimental design questions, delivering interpretable reports, and pushing back on underpowered study designs.

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 specific consultations where they reshaped a study design or explained a QC failure that bench colleagues then acted on.

Evidence-led prompts

Interview questions for a Bioinformatics Specialist

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

  1. 01

    Have you used bioinformatics software suites or pipelines? Which ones?

  2. 02

    Which programming languages are you proficient in for bioinformatics work?

  3. 03

    What experience do you have with next-generation sequencing data?

  4. 04

    How do you handle large datasets and ensure data integrity?

  5. 05

    How do you prioritise tasks in a fast-moving research environment?

See the complete Bioinformatics Specialist question set
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