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
Probe depth on matching algorithms and thresholds: FAR/FRR and ROC tuning, ISO/IEC 19794 template formats, ISO/IEC 30107-3 presentation attack detection, ABIS/AFIS platforms, FIDO2 or liveness stacks.
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
Explains threshold tuning trade-offs with real FAR/FRR figures, names PAD levels tested, and cites vendor engines or FRVT benchmark results.
02
Evaluation factor
Real incidents and findings
30% weight
Ask for specific spoof or enrolment fraud cases handled: silicone finger or deepfake face attempts, duplicate identity detection, false match escalations, and what the forensic review concluded.
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 named incidents with attack vector, detection signal, match scores reviewed, and the enrolment or algorithm change that followed.
03
Evaluation factor
Risk judgement
20% weight
Test how they weigh convenience against security: threshold changes affecting throughput, demographic differential error rates, template storage decisions under GDPR Article 9, BIPA, or retention policy.
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
Frames decisions around measured error rates by cohort, data minimisation, and irrevocability of biometric templates rather than blanket tightening.
04
Evaluation factor
Getting things fixed
15% weight
Check follow-through on remediation: driving vendor algorithm upgrades, rewriting enrolment SOPs, retraining operators, and evidencing fixes in audits or ISO 27001 and privacy impact assessments.
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
Tracks findings to closure with named owners, retested match performance, and updated DPIAs or enrolment procedures signed off by stakeholders.
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