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Analyze user activity data to identify scammer and fraud modus operandi, surfacing patterns across listings, accounts, messaging, and transactions
Own the full lifecycle of detection rules on internal risk systems: propose, test, deploy, and iterate — in close collaboration with engineering and operations teammates
Translate investigation findings into concrete detection signals and rule logic; work directly with engineers and data scientists to implement them
Monitor performance of active rules (precision, recall, false-positive rate) and proactively tune based on results
Investigate emerging abuse patterns end-to-end: from data exploration to root cause analysis to recommended response
Partner with operations (trust analysts, CS) to turn frontline observations into analytical hypotheses
Build and maintain dashboards and reports to track fraud trends, rule coverage, and team KPIs
Participate in cross-functional reviews with Product and Engineering on fraud tooling and model development
