Product Manager, Fraud Data

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)