Staff Product Manager, Risk Modeling

Plaid Plaid · Fintech · San Francisco, CA · All Departments

Staff Product Manager for Plaid's internal network-health and risk platform, focusing on detecting, scoring, and acting on risky traffic across partner integrations. The role involves setting the roadmap, defining decisioning frameworks, and partnering with Data Science and MLE teams to improve detection precision and translate model outputs into real-time product actions. This is a platform PM role with the network itself as the customer, aiming for a measurably safer, higher-quality data supply.

What you'd actually do

  1. Define and prioritize the DS/MLE roadmap for network-health detection — articulate model investment needs, own precision/recall tradeoff decisions, and translate model outputs into real-time product actions.
  2. Build and own the data-driven framework for real-time block-vs-allow decisions, balancing network quality against customer and commercial impact.
  3. Aggregate requirements across risk engineering, fraud & abuse operations, and data partners into a single prioritized roadmap.
  4. Define how to measure Network Health
  5. You define and own the team’s OKRs (risky-traffic detection/step-up; ATO reduction, Authentication correctness).

Skills

Required

  • 6+ years in product management, ideally on internal risk, trust & safety, fraud, or identity platforms.
  • Hands-on technical depth: detection signals, precision/recall tradeoffs, light data analysis/investigation.
  • Experience building ML based products / insights
  • Proven partnership with Data Science / MLE teams on risk models.
  • Experience with risk decisioning, KYC, synthetic/stolen-identity detection, or ATO.
  • Clear product requirements; strong cross-functional alignment.

Nice to have

  • Experience in a sophisticated internal risk org at a large fintech/bank/marketplace.
  • Former risk analyst / DS / investigator who moved into PM; familiarity with bank data / open finance.

What the JD emphasized

  • risk modeling
  • detection, scoring, and real-time decisioning
  • ML based products / insights
  • risk decisioning
  • synthetic/stolen-identity detection

Other signals

  • risk modeling
  • detection, scoring, and real-time decisioning
  • ML based products / insights
  • risk decisioning
  • identity detection