Data Analyst (Trust & Safety)

Job Description

• 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

Qualifications

Competencies & Prior Experience

Analytics & Investigation

• Strong SQL skills — comfortable writing complex queries for exploratory analysis and production monitoring

• Experience analyzing behavioral or transactional datasets to detect anomalies, patterns, or abuse signals

• Logical, structured problem-solving: able to decompose ambiguous fraud patterns into testable hypotheses

Technical Fluency

• Working knowledge of how ML/data science models function (not a builder, but a capable consumer and collaborator)

• Comfortable with APIs, data pipelines, and understanding what data points drive what model or rule outcomes

• At ease in a terminal environment; uses AI tools (e.g., Claude, Cursor) as a natural part of daily workflow

Trust & Safety Domain

• Exposure to fraud, scam, or abuse detection in a marketplace, fintech, or platform context preferred

• Understanding of common fraud typologies: account manipulation, fake listings, social engineering, payment fraud

• Familiarity with rule-based detection systems or risk scoring frameworks

Working Style

• Genuinely curious about fraud and adversarial behavior — not just processing tickets, but interested in how scammers operate

• Proactive: identifies problems and proposes solutions without waiting to be told

• Effective communicator with both technical (engineers, data scientists) and non-technical (operations, leadership) stakeholders

• Comfortable with ambiguity and fast iteration in an early-stage fraud tooling environment

Additional Information

By proceeding with your application, you are adhering to our PDPA policies. In case you are interested to know  more, read about our Candidates Personal Data Privacy Statement. 

By proceeding with your application, you are adhering to our PDPA policies. In case you are interested to know more, read about our Candidates Personal Data Privacy Statement.