# Staff Product Manager, Risk Modeling

**Company:** [Plaid](https://hotfix.jobs/companies/plaid)
**Location:** San Francisco, CA, New York, NY
**Role:** Product Management
**Salary:** $208k – $307k/yr
**Experience:** 6+ years
**Skills:** Product Management, Risk Modeling, Machine Learning, Data Science, Precision/Recall Tradeoffs, Risk Decisioning, Kyc, Fraud Detection, Data Analysis, Okrs, Roadmap Prioritization
**Posted:** 2026-06-30

> 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.

## Job Description

## 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.
- Clear product requirements; strong cross-functional alignment.

## Nice-to-Haves
- 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.

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