Senior Product Manager - Fraud & Risk

Expedia Expedia · Hospitality · Seattle, WA

The Senior Product Manager will own the strategy and delivery of products that protect Expedia Group and its travelers from fraud, abuse, and financial crime. This role involves defining product vision, building roadmaps, and leading AI/ML detection capabilities. Responsibilities include collaborating with engineering and data science to build real-time, low-latency fraud detection and risk-scoring systems, operating in adversarial environments, and defining/monitoring key KPIs. The role requires experience in product management focused on fraud/risk and familiarity with AI/ML concepts applied to products.

What you'd actually do

  1. Define and drive the product vision and strategy for holistic fraud prevention and risk management capabilities across Expedia Group’s ecosystem, clearly articulating loss‑versus‑friction trade‑offs to senior stakeholders in growth, finance, operations, and legal.
  2. Build and own an integrated, data‑driven roadmap for assigned fraud and risk products where each milestone is tied to measurable impact on fraud loss rate, insult rate, trust damage, chargebacks, approval/failure rates, and review‑queue cost, and defend prioritization in the face of competing business objectives informed by global market dynamics.
  3. Lead collaboration with engineering, data science, and analytics to build or buy real‑time, low‑latency fraud detection and risk‑scoring systems, including AI/ML‑driven anomaly detection, graph and entity‑linking, behavioral analytics, and pattern recognition while managing rule complexity and technical debt.
  4. Operate effectively in ambiguous, adversarial environments by independently modeling dollars at stake, applying investigative techniques such as attack‑vector discovery, adversarial red‑teaming, and fraud‑ring analysis, and translating insights into scalable, production‑ready product capabilities.
  5. Define, monitor, and communicate key fraud and risk KPIs, run rigorous experimentation (including champion/challenger tests and holdouts), and translate detection performance and AI/ML model insights into clear narratives and recommendations that drive organizational alignment and product decisions.

Skills

Required

  • Product management roles focused on fraud, risk, trust & safety, seller fraud, or payments
  • Proven end‑to‑end ownership of fraud and risk products or platforms
  • Demonstrated experience collaborating with engineering and data science teams to design, launch, and iterate on fraud detection and risk‑scoring solutions
  • Strong fluency in data‑driven decision making, data science principles, statistical reasoning, and agile delivery practices
  • Familiarity with AI‑driven systems, tools, or workflows and applying AI/ML concepts to real world products

Nice to have

  • Deep domain expertise across fraud, payments, supplier fraud, chargebacks, dispute management, and AML/KYC fundamentals
  • Track record of shaping product‑level or domain‑level fraud and risk direction informed by deep competitive insights
  • Independently modeling financial impact to inform strategy and prioritization
  • Significant experience partnering with engineering and data science to design and enhance AI/ML‑enabled fraud detection and risk‑scoring capabilities (including anomaly detection, graph‑based methods, and behavioral analytics)
  • Safely integrates and operates AI/ML‑enabled solutions that improve outcomes in large‑scale production environments
  • Demonstrated ability to analyze and reverse‑engineer fraud patterns and adversarial behaviors
  • Employing investigative approaches such as attack‑vector discovery, fraud‑ring analysis, and adversarial red‑teaming
  • Translating these insights into robust product features, decision flows, and controls
  • Proven effectiveness influencing and aligning senior stakeholders

What the JD emphasized

  • hard tradeoffs
  • adversarial environments
  • AI/ML-driven anomaly detection
  • graph and entity-linking
  • behavioral analytics
  • pattern recognition
  • attack-vector discovery
  • adversarial red-teaming
  • fraud-ring analysis
  • AI/ML-enabled fraud detection and risk-scoring capabilities

Other signals

  • AI/ML detection capabilities
  • real-time, low-latency fraud detection and risk-scoring systems
  • AI/ML-driven anomaly detection
  • graph and entity-linking
  • behavioral analytics
  • pattern recognition
  • AI/ML-enabled fraud detection and risk-scoring capabilities