Interview scorecard template

Genetic Data Analyst interview scorecard

Evaluate Genetic Data Analyst 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 variant calling and annotation: BWA or Minimap2, GATK best practices, VEP or ANNOVAR, plus scripting in Python, R, and bash on. Use the rubric to compare role-specific evidence consistently.

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software dataacmg classificationbioinformaticsngs pipelinesvariant calling
TL;DR
For technical proficiency, look for evidence the candidate names specific pipeline steps, filtering thresholds, and reference builds (GRCh37 versus GRCh38) used, and explains why each tool was chosen. For systems and trade-offs, look for evidence the candidate discusses concrete trade-offs such as gVCF versus single-sample calling, coverage depth targets, and where they accepted sensitivity loss for turnaround. 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 variant calling and annotation: BWA or Minimap2, GATK best practices, VEP or ANNOVAR, plus scripting in Python, R, and bash on Slurm or cloud batch.

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 pipeline steps, filtering thresholds, and reference builds (GRCh37 versus GRCh38) used, and explains why each tool was chosen.

02
Evaluation factor

Systems and trade-offs

25% weight

Probe how they handled scale and reproducibility: WDL or Nextflow workflows, joint genotyping across thousands of samples, storage of BAM and VCF, runtime versus cost decisions.

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

Discusses concrete trade-offs such as gVCF versus single-sample calling, coverage depth targets, and where they accepted sensitivity loss for turnaround.

03
Evaluation factor

Evidence and rigour

25% weight

Assess statistical rigour: population structure correction in GWAS, multiple testing control, coverage and contamination QC metrics, ACMG criteria or ClinVar evidence weighting for variant classification.

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

Cites QC metrics they gated on (Ti/Tv, call rate, het/hom ratio) and shows scepticism toward findings lacking orthogonal confirmation.

04
Evaluation factor

Collaboration and communication

15% weight

Look for work with wet lab staff, clinical geneticists, or PIs: handling ambiguous requests, reporting incidental findings, and documenting analyses so results survive audit or reanalysis.

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 translating variant results for clinicians or reviewers, and maintains versioned notebooks or reports others reran without help.

Evidence-led prompts

Interview questions for a Genetic Data Analyst

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

  1. 01

    What experience do you have analysing large-scale genetic datasets?

  2. 02

    Do you have experience with genome-wide association studies?

  3. 03

    Can you describe a challenging analysis project and how you approached it?

  4. 04

    What statistical methods do you use for analysing genetic data?

  5. 05

    Describe your experience with machine learning applied to genetic data?

See the complete Genetic Data Analyst question set
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