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 how they build and tune detection models: gradient boosting or graph features, false positive rates, precision at top-k alerts, feature stores, real-time scoring latency in tools like Feedzai, Sift or in-house Python 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
Names specific model types, features and thresholds used, and quotes their own false positive and detection rate figures with context.
02
Evaluation factor
Real incidents and findings
30% weight
Ask for actual fraud rings or attack patterns they caught: account takeover, synthetic identity, first-party chargeback abuse, mule networks, and what the loss avoided or chargeback rate change was.
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
Walks through named cases from alert to confirmed loss figure, including how the pattern evaded the previous rule set.
03
Evaluation factor
Risk judgement
20% weight
Test how they weigh customer friction against fraud loss: when they loosen a rule, decline rate impact, appetite set with the business, SAR filing calls, and model drift monitoring cadence.
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 as explicit loss versus friction trade-offs with numbers, and shows where they deliberately accepted fraud to protect conversion.
04
Evaluation factor
Getting things fixed
15% weight
Check how detections became production controls: rule deployments with engineering, analyst feedback loops into labels, model documentation for model risk review, and handoffs to investigations or compliance teams.
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
Describes shipped rules and retrained models with owners, review sign-off, and evidence the alert queue quality improved afterwards.
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