Lead, Advanced Analytics, Fraud and Safety Operations

About the role

This role focuses on leveraging advanced analytics to address fraud and safety challenges within Airbnb's platform. A significant part of the work involves developing self-service data tools, enabling non-technical teams to conduct in-depth analyses and make data-backed decisions independently. The Lead will also be responsible for creating clear, compelling dashboards and narratives to present insights to executives and various cross-functional teams.

Success in this position means ensuring that fraud and safety metrics are robust, scalable, and supported by strong governance and automated monitoring. The role involves owning the launch and decision criteria for fraud and safety experiments, defining thresholds, and ensuring data quality to guide leadership in making informed choices. This includes supporting external audits, law enforcement requests, and board-level reporting with precise data and analytical explanations.

The Lead will also operationalize frameworks to quickly assess the impact of fraud incidents on the platform, reputation, and regulatory compliance. This involves enabling rapid escalation, conducting thorough retrospectives, and fostering systematic learning from incidents. The goal is to empower legal, policy, operations, product, and engineering teams to make proactive, data-driven decisions through accessible self-service tools.

The base pay for this role is between $164,000 and $191,000 USD, with potential eligibility for bonus, equity, benefits, and Employee Travel Credits.

Skills that matter here

• data analytics: This role requires deep expertise in data analytics to identify and mitigate fraud and safety risks through data-driven insights.
• SQL: Strong SQL skills are essential for data modeling and querying to support analytical work and self-service tool development.
• Python: Familiarity with Python is needed for data manipulation, analysis, and potentially for building analytical tools or models.
• R: Familiarity with R is also beneficial for statistical analysis and data visualization in addressing fraud and safety issues.
• ML pipelines: A working knowledge of ML pipelines is required to integrate machine learning solutions into fraud and safety detection and prevention efforts.
• causal inference methods: Experience in designing experiments and applying causal inference methods is crucial for understanding the impact of interventions in a multi-sided platform.

Who this role suits

• A person who thrives on translating complex data into clear, actionable insights for diverse audiences, from technical teams to executives.
• Someone with a proven track record of building and owning large-scale data products or taxonomies, focusing on self-service solutions.
• An individual who possesses a deep understanding of how to statistically measure rare events and apply rigorous methods to ensure data quality.
• A candidate skilled in incident impact scoping, post-incident analytics, and scenario planning, capable of driving systematic improvements.

From the employer

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.

How We'll Take Care of You:

• Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands.
• The base pay range is subject to change and may be modified in the future.
• This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.
• Pay Range $164,000 — $191,000 USD.