Product Manager, Fraud Data

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. The Fraud team is building the largest network of trusted identities in the US. Our products span the full user lifecycle: Identity Verification and Monitor for onboarding and compliance, Layer for frictionless returning-user experiences, and Protect, our fraud intelligence platform, as the connective risk layer for all of it. Protect has more inbound demand than any product we have launched. The challenge is signal. We are building the data infrastructure that will make Protect the best fraud signal in financial services. This is a product role for someone who lives at the intersection of fraud expertise and data science. You will work directly with our data team to build and improve Protect's signal stack: the attributes, scores, and fraud vector intelligence that customers use to make real-time decisions. You need to understand fraud, know how to partner with data scientists as a peer, and be able to translate between what models need and what products should deliver. 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. Qualifications: