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 in abuse detection stacks: rule engines, ML classifiers for spam or CSAM hashing (PhotoDNA), graph clustering for coordinated accounts, and SQL or Python for signal mining.
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 detection pipelines they built, explains feature signals used, and discusses precision, recall, and false positive costs concretely.
02
Evaluation factor
Real incidents and findings
30% weight
Probe actual enforcement work: a fraud ring or bot network they dismantled, a policy violation surge they triaged, ticket volumes handled, and takedown or appeal outcomes.
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 a named abuse wave end to end with detection timeline, accounts actioned, and measurable reduction in violative content.
03
Evaluation factor
Risk judgement
20% weight
Assess how they weigh enforcement harm against user harm: over-blocking legitimate users, gray-area policy calls, regional legal duties like DSA or NetzDG, and escalation thresholds.
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
Articulates clear thresholds for automated versus human review and cites a case where they deliberately loosened or tightened enforcement.
04
Evaluation factor
Getting things fixed
15% weight
Look for follow-through with policy, legal, and product teams: shipping model retrains, closing appeals backlogs, writing runbooks, and reducing moderator queue latency.
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
Shows durable fixes adopted by other teams, such as a shared signal service or runbook that cut repeat abuse.
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