# Product Manager, Fraud Data

**Company:** [Plaid](https://hotfix.jobs/companies/plaid)
**Location:** New York, NY, San Francisco, CA
**Role:** Product Management
**Salary:** $191k – $263k/yr
**Skills:** Fraud Prevention, Fraud Detection Systems, Risk Signal Evaluation, Data Science Collaboration, Ml Model Inputs/Outputs, Feature Engineering, Model Evaluation, Label Quality Assessment, Precision/Recall Analysis, Product Roadmap Management
**Posted:** 2026-06-23

> Own the roadmap and labeling pipeline for Plaid Protect's fraud intelligence platform. Partner daily with data scientists to define fraud vector scores and translate model outputs into customer-facing product features.

## Job Description

## Responsibilities
- Own the feature roadmap for Protect: what we build, how we validate it, and how we measure coverage and fill rates
- Build and manage the labeling pipeline as a product, including customer retros, data partnerships, and the label feedback loop into model training
- Define the product requirements for fraud vector scores, including signal selection, precision/recall targets, and customer-facing output design
- Partner with data scientists and MLEs as a day-to-day collaborator, not just a requester. Translate between model needs and product requirements in both directions
- Work with GTM and Protect PM on how attribute improvements and new scores translate into customer-facing value propositions

## Requirements
- Real hands-on fraud prevention experience: fraud vectors, detection systems, risk signal evaluation
- Direct experience collaborating with data scientists or ML engineers to ship data-driven products
- Ability to talk to a DS about feature distributions, label quality, and model performance

## Nice to Have
- Experience at a fraud vendor (SentiLink, Sardine, Socure, Persona, Incode) or a fraud operations team at a large fintech or bank
- Background with ML model inputs/outputs: feature engineering, offline evaluation, and moving from experimentation to production
- Specialization in a specific fraud vector (ATO, synthetic identity, or first-party fraud)

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