Staff Product Manager - Fraud

Plaid · Fintech · San Francisco, CA · All Cost Centers

Product Manager for Plaid's Fraud team, focusing on Identity Verification (IDV) and Monitor (AML) products. This role owns the foundation of Plaid's fraud platform, aiming to improve onboarding, prevent bad actors, and ensure compliance at scale. The PM will set strategy, drive execution, and partner with engineering and data teams to deliver accurate, reliable, and scalable fraud prevention systems.

What you'd actually do

  1. Set the direction. Own the vision, strategy, and roadmap for IDV and Monitor end to end. Drive meaningful outcomes for fraud prevention, compliance, and customer experience.
  2. Build the backbone. Grow IDV and Monitor as the core of Protect and Layer by expanding data coverage, improving decision quality, and enhancing reliability, latency, and transparency across systems
  3. Execute with focus. Partner closely with engineering, design, and other Fraud teams to move from concept to production with clear launch criteria and consistent follow-through.
  4. Communicate with precision. Create clarity across teams by articulating decisions, tradeoffs and rationale crisply. translating complex systems into simple narratives and keeping partners aligned on what matters.
  5. Stay close to the field. Engage with customers and GTM teams to spot fraud and compliance patterns early, translate them into product improvements, and maintain momentum through demos, integrations, and launches.

Skills

Required

  • 6+ years of product management experience in fraud, risk, security, or identity
  • Strong technical foundation
  • Experience building or integrating fraud detection, risk scoring, or IDV/AML systems
  • Ability to write clear product requirements
  • communicate technical concepts to diverse audiences
  • Comfortable working closely with engineering, data, and go-to-market teams to deliver measurable impact

Nice to have

  • Experience with data science or ML-driven systems, including model inputs and outputs
  • Entrepreneurial or founder experience with a bias toward ownership and experimentation

What the JD emphasized

  • fraud
  • risk
  • security
  • identity
  • fraud detection
  • risk scoring
  • IDV/AML systems
  • data science or ML-driven systems