Lead, Advanced Analytics, Fraud and Safety Operations

Responsibilities

• Build self-service data tools that empower non-technical teams to ask deep questions, run “what if” analyses, and generate actionable, data-backed outcomes
• Craft compelling narratives and dashboards that surface insights to executives and cross-functional teams
• Ensure fraud and safety metrics are future-proof, scalable and supported by clear governance, ownership and automated monitoring
• Own launch and decision criteria for fraud and safety experiments by defining launch thresholds, gating metric releases on decision quality, and helping leadership make data-driven decisions
• Support external audits, law-enforcement requests, and board-level reporting with rigorous, well-governed data and clear analytical narratives
• Operationalize frameworks that instantly assess and size the platform, reputational and regulatory impact of fraud incidents, enabling rapid escalation, crystal-clear retrospectives and systematic learning

Requirements

• 5+ years of experience in data analytics, fraud, safety, or a related quantitative domain, with deep individual-contributor expertise, or 2+ years of industry experience with a PhD
• Proven ownership of large-scale data products or taxonomies
• Strong SQL and data-modeling expertise; familiarity with Python/R; working knowledge of ML pipelines
• Strong experience designing experiments and applying causal inference methods, ideally in a multi-sided platform setting
• Deep understanding of how to measure rare events with statistical rigor, including prevalence estimation, sampling strategy, and statistical power
• Familiarity with account integrity, user authentication and connected-account vectors, such as social logins, device fingerprinting and related identity signals
• Skilled in incident impact scoping, post-incident analytics, scenario planning or tabletop exercises, and translating insights into systematic improvements
• Track record of enabling legal, policy, ops, product, and engineering teams to make independent, forward-facing, data-driven decisions via self-service tools
• Exceptional storyteller with the ability to make complex analytics actionable for every audience

Nice-to-Haves

• Prior work in marketplace, fintech, or travel/hospitality tech environments
• Familiarity with real-time decision engines, graph analytics, and anomaly-detection frameworks
• Exposure to adjacent trust / risk domains, such as identity verification, fraud, chargebacks, or financial risk management
• Graduate degree (MS/PhD) in statistics, economics, computer science, data science, operations research or another quantitative field