Staff Product Manager, Platform Operations

Robinhood Robinhood · Fintech · Menlo Park, CA +1 · Customer Trust and Safety

Staff Product Manager for Robinhood's Operations Platform team, focusing on building and maintaining critical infrastructure, tooling, and automation for Customer Trust & Safety Operations. The role involves owning product strategy for case management, ML/AI alert resolution, operational readiness for new products/geos, and customer experience during fraud controls. The goal is to detect and mitigate fraud in real-time, protect customer assets, and ensure a secure, trusted platform.

What you'd actually do

  1. Set the long-term vision, strategy, and priorities for the platform team supporting Robinhood's Fraud and Account Operations.
  2. Own the multi-year product roadmap for the case-management and investigation tooling, to prevent complex fraud vectors and protect customer assets.
  3. Drive AI-based automation of fraud alert resolution and account lifecycle claims, owning the leadership metrics tracked against these goals.
  4. Reduce friction and ticket volume for customers affected by fraud controls, partnering on messaging, self-serve flows, and policy clarity.
  5. Architect and implement foundational services, policies, and system controls to ensure Robinhood remains a secure, trusted platform for financial activity.

Skills

Required

  • 8+ years of product management experience
  • building and shipping internal tools, fraud/risk operations platforms at scale
  • Direct experience shipping AI/ML-based automation
  • expertise in leveraging product analytics to guide critical product decisions
  • Proven ability to synthesize complex, ambiguous problems into clear, executable strategies
  • Strong technical depth with case management systems, workflow automation platforms or risk decisioning engines
  • High self-motivation with a track record of driving complex initiatives from conception to completion

Nice to have

  • Bachelor’s or Master’s Degree, or equivalent practical experience

What the JD emphasized

  • building and shipping internal tools, fraud/risk operations platforms at scale
  • shipping AI/ML-based automation
  • Strong technical depth with case management systems, workflow automation platforms or risk decisioning engines

Other signals

  • AI/ML automation for fraud alert resolution
  • Scalable case management and investigation solutions
  • Multi-modal user experiences for fraud controls
  • Foundational services, policies, and system controls for security and trust