Lead, Advanced Analytics, Fraud and Safety Operations

Airbnb Airbnb · Consumer · United States · Analytics

Lead, Advanced Analytics role focused on building systems to mitigate fraud and safety risks at Airbnb. This involves designing and deploying systems, defining metrics, creating data narratives, running experiments, and building tools at the intersection of policy, operations, and risk. The role emphasizes enabling non-technical teams with self-service data tools, creating scenario simulators, and operationalizing frameworks for incident assessment.

What you'd actually do

  1. Build self-service data tools that empower non-technical teams to ask deep questions, run “what if” analyses, and generate actionable, data-backed outcomes without gatekeeping
  2. Craft compelling narratives and dashboards that surface insights to executives and cross-functional teams.
  3. Ensure fraud and safety metrics are future-proof, scalable and supported by clear governance, ownership and automated monitoring
  4. 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
  5. Support external audits, law-enforcement requests, and board-level reporting with rigorous, well-governed data and clear analytical narratives.
  6. 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

Skills

Required

  • 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
  • working knowledge of ML pipelines
  • Strong experience designing experiments and applying causal inference methods
  • 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 have

  • 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
  • Python/R

What the JD emphasized

  • deep individual-contributor expertise
  • Proven ownership of large-scale data products or taxonomies
  • Strong SQL and data-modeling expertise
  • Strong experience designing experiments and applying causal inference methods
  • Deep understanding of how to measure rare events with statistical rigor
  • Skilled in incident impact scoping, post-incident analytics, scenario planning or tabletop exercises
  • 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

Other signals

  • build systems that mitigate fraud and safety risks at scale
  • build always-on scenario simulators for fraud and safety
  • turn incident impacts into seamless signals for continuous improvement
  • operationalize frameworks that instantly assess and size the platform, reputational and regulatory impact of fraud incidents