Own Plaid's internal risk and network-health platform. Define DS/MLE roadmaps for detection/scoring/decisioning, own precision-recall and risk-vs-conversion tradeoffs, partner with Data Science/MLE teams, and set OKRs for risky traffic reduction across data-partner, authentication, and fraud risks. Requires 6+ years PM experience with hands-on technical depth in ML-based risk products.
208k – 307k/yr
Hybrid6+ YOEProduct Management
About the role
Responsibilities
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.
Build and own the data-driven framework for real-time block-vs-allow decisions, balancing network quality against customer and commercial impact.
Aggregate requirements across risk engineering, fraud & abuse operations, and data partners into a single prioritized roadmap.
Define how to measure Network Health.
Own the team’s OKRs (risky-traffic detection/step-up; ATO reduction, Authentication correctness).
Define and recommend Plaid’s risk-versus-conversion posture, partnering with company leadership to set clear, data-backed thresholds for when to allow, step up, or block traffic.
Get hands-on with data: run light investigations yourself, pull signals, and ground product decisions in evidence.
Span multiple risk problem categories simultaneously: data-partner supply risk, authentication risk/correctness, and AQO — owning one coherent roadmap across all three.
Requirements
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.
Own Plaid's internal risk and network-health platform as a technical PM. Set the DS/MLE roadmap for detection and scoring, own real-time block/allow decision frameworks, partner closely with Data Science and ML Engineering teams, and define OKRs around risky traffic reduction and authentication correctness. Requires 6+ years PM experience with hands-on ML/risk modeling background.
208k – 307k/yr
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