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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 scaling how Fraud & Safety operations are measured, governed, and optimized globally. As Senior Manager, Analytics – Fraud & Safety Investigations, you will lead a team of analysts supporting human enforcement systems across content moderation, fraud review, and safety operations. You will own how operational performance is measured and improved—translating complex enforcement workflows into scalable KPI frameworks, experimentation roadmaps, and decision systems. This role sits at the intersection of Operations, Policy, Product, and Engineering, ensuring that human enforcement is efficient, consistent, and aligned with platform safety goals.
Job Responsibility:
Define and operationalize KPI frameworks for global Fraud & Safety operations (quality, accuracy, SLA, productivity, cost)
Build dashboards and reporting systems to monitor enforcement performance across regions, vendors, and workflows
Lead weekly/monthly business reviews, translating data into actionable insights for Ops, Policy, and leadership
Design and lead experimentation (A/B testing, pilots) to improve enforcement quality, reviewer productivity, and user experience
Identify drivers of errors, escalations, and policy inconsistencies, and translate findings into process improvements
Partner with Ops and Policy to test and refine enforcement strategies (e.g., thresholds, escalation rules)
Partner with global operations and vendor teams to optimize staffing, capacity planning, and queue management
Analyze performance across 3P vendors and internal teams, ensuring SLA adherence and quality standards
Drive forecasting models to balance cost, coverage, and service levels across high-volume workflows
Partner with Product and Engineering to translate operational insights into tooling and workflow improvements
Collaborate with Policy and Legal to ensure enforcement decisions are measurable, auditable, and compliant
Support high-severity incidents and 'war room' analytics, providing real-time insights during escalations
Build self-service analytics tools enabling Ops leaders to monitor performance and run scenario analyses independently
Standardize metrics, taxonomies, and reporting across workflows to create a consistent 'source of truth' for operations
Drive adoption of data-driven decision-making across global Trust & Safety teams
Requirements:
8+ years of experience in analytics, risk, or safety
5+ years leading high-performing teams
Proven ownership of large-scale data products or taxonomies in a regulated environment
Experience supporting large-scale human operations (content moderation, fraud review, support, or risk ops)
Strong SQL & data-modeling expertise
experimentation design, working knowledge of Python/R, ML pipelines
Proven ability to: Design KPI's and operational metrics
Translate ambiguous problems into structured analytical frameworks
Drive measurable improvements in quality, efficiency, and cost
Skilled in incident impact scoping, post-incident analytics, and scenario planning or tabletop exercises—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
Exceptional storyteller with a flair for making complex analytics actionable for every audience, and for championing transformation deep into the organization
Background in Trust & Safety, Integrity, Fraud, or Risk operations
Familiarity with real-time decision engines, and anomaly-detection frameworks
Experience leading hybrid teams (onsite/remote) and managing third-party analytics vendors
Statistics or quantitative graduate degree (MS/PhD)
Experience with: Workforce planning & forecasting
Experimentation platforms
Real-time operations analytics
Nice to have:
Background in Trust & Safety, Integrity, Fraud, or Risk operations
Familiarity with real-time decision engines, and anomaly-detection frameworks
Experience leading hybrid teams (onsite/remote) and managing third-party analytics vendors
Statistics or quantitative graduate degree (MS/PhD)