Lead, Advanced Analytics, Fraud and Safety Operations

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. The Community You Will Join: Airbnb is entering a new era—reimagining how fraud, safety, and quality are measured, protected, and elevated across the digital landscape. For the role of Lead, Advanced Analyst we’re looking for a senior individual contributor and subject-matter expert who can set a high analytical standard for the team. You will collaborate with a talented cross-functional team of operations specialists, product managers, data scientists and engineers to design and deploy systems that mitigate fraud and safety risks at scale. You will define and monitor metrics, create data narratives, design and run experiments, and build tools that drive decisions at the intersection of policy, operations, and risk. The Difference You Will Make: This role isn’t just about reporting what happened; it’s about building the systems that help Airbnb see around corners. You will democratize data access, build always-on scenario simulators for fraud and safety, and turn incident impacts into seamless signals for continuous improvement. Your insights, frameworks and systems will empower stakeholders across Legal, Operations, Policy, Product and Engineering to make bold, data-driven and context-aware decisions in real time. A Typical Day: - 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 - 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 Your Expertise: - 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 Preferred Experience - 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 Your Location: This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from. Our Commitment To Inclusion & Belonging: Airbnb is committed to working with the broadest talent pool

Your Expertise: 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. Preferred Experience: 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.