Lead, Advanced Analytics, Fraud and Safety Operations

Airbnb

7d•$164,000 - $191,000•Remote

About The Position

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. 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.

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

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 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