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Staff Product Manager, Risk Modeling

Plaid·US·San Francisco HQ·senior
data science
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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. Team: We protect the integrity of Plaid's network and authentication surfaces. We keep bad actors, synthetic and stolen identities, and low-quality data-partner traffic off the platform so that the millions of connections Plaid powers stay trustworthy. We build Plaid's internal risk platform: detection, scoring, and real-time decisioning and we're the foundation every other Plaid product depends on Role: You own Plaid's internal network-health and risk platform — how we detect, score, and act on risky traffic across our 10,000+ data-partner integrations. You set the roadmap, define the block/step-up/allow decisioning framework, and partner directly with the Data Science and MLE and operations team to sharpen detection precision. This is a platform PM role: your customer is the network itself, and your output is a measurably safer, higher-quality data supply. 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 cus

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