security compliancecloud pipeline hardeningdbgap controlled accessgenomic data securityhipaa gdpr
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 depth
35% weight
Check depth across both sides: read alignment and variant pipelines (Nextflow, Snakemake, GATK) plus IAM scoping, encryption of BAM/VCF stores, HPC and cloud bucket hardening.
Evidence to listen for
Command of the specific attack surface, tooling, and controls the role covers
Understands how the underlying system works, not just how the tool reports on it
Can explain an attack or control chain end to end
Distinguishes what they found themselves from what a scanner flagged
Five-point scoring guide
1
Poor
Tool operator only; no understanding of the systems underneath.
2
Needs Improvement
Runs tooling but cannot explain findings or how the attack works.
3
Satisfactory
Solid working knowledge; depth thins outside familiar tooling.
4
Very Good
Strong command of the domain; explains attack and control chains clearly.
5
Excellent
Names specific controls applied to genomic workloads: KMS key policies on VCF buckets, scoped service accounts, audit logging on Nextflow runs.
02
Evaluation factor
Real incidents and findings
30% weight
Probe actual events they handled: exposed dbGaP or UK Biobank data, credential leaks in Git repos with pipeline configs, misconfigured S3 buckets holding patient sequence data.
Evidence to listen for
Brings specific incidents, findings, or audits they personally worked
States their own role rather than the team's
Describes what was actually at risk and what changed afterwards
Can talk about a finding that turned out to be wrong
Five-point scoring guide
1
Poor
No hands-on work; knowledge is entirely certification or coursework.
2
Needs Improvement
Limited exposure; cannot describe their contribution to an incident.
3
Satisfactory
Real casework with adequate detail; ownership sometimes vague.
4
Very Good
Specific incidents with clear personal scope and what changed after.
5
Excellent
Recounts a concrete incident with timeline, containment steps, data subjects affected, and the reporting path to the IRB or data access committee.
03
Evaluation factor
Risk judgement
20% weight
Assess how they weigh re-identification risk against research velocity: beacon queries, allele frequency leakage, aggregate release thresholds, and when to demand a trusted research environment.
Evidence to listen for
Prioritises by actual exploitability and business impact, not raw severity scores
Can argue for accepting a risk as well as fixing it
Knows the difference between a finding and a problem
Does not cry wolf or wave things through
Five-point scoring guide
1
Poor
Treats every finding as critical, or waves real risk through.
2
Needs Improvement
Follows severity scores mechanically; no business context.
3
Satisfactory
Reasonable prioritisation; less confident arguing for risk acceptance.
4
Very Good
Prioritises by exploitability and impact; can justify accepting a risk.
5
Excellent
Argues risk with reference to re-identification literature and DUA terms, and explains a case where they permitted access with compensating controls.
04
Evaluation factor
Getting things fixed
15% weight
Look for evidence they moved bioinformaticians, not just filed tickets: policy-as-code in CI, container image scanning adopted, sign-off cycles with data access committees shortened.
Evidence to listen for
Writes findings engineers can act on rather than a wall of output
Has persuaded a team to fix something they did not want to fix
Explains risk to executives in business terms
Works with the org rather than policing it
Five-point scoring guide
1
Poor
Adversarial with engineering; findings never get fixed.
2
Needs Improvement
Reports are unactionable; no influence beyond raising tickets.
3
Satisfactory
Adequate reporting; relies on mandate rather than persuasion.
4
Very Good
Actionable findings and a real record of getting fixes shipped.
5
Excellent
Cites remediation that stuck, such as secrets scanning added to pipeline repos, with adoption rates and pushback they worked through.
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