Fraud Researcher

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. Our Fraud team's mission is to help companies detect and prevent fraud using Plaid's financial network data. We believe that transaction patterns, device signals, identity linkages, and behavioral data are dramatically underleveraged tools in fraud prevention. Our products — including Protect and Signal — operate at network scale and depend on real-world investigation and research to stay ahead of adaptive adversaries. As a Senior Fraud Researcher, you will sit at the intersection of live fraud investigation, applied data science, and product innovation. You will lead complex investigations, translate findings into detection improvements, and collaborate tightly with Data Science, ML, and Product teams to shape the next generation of Plaid's fraud capabilities. This is not a purely operational role — your research directly drives features, model inputs, and product design. Responsibilities: Live Fraud Investigation & Reconstruction Lead investigations into complex fraud cases across identities, accounts, devices, and transaction surfaces Provide support to day-to-day fraud operations including SEVs and alert triage Reconstruct attacker sequences and hypothesize actor intent and tooling Distill patterns from noisy signals into clear narratives and actionable insights Bridge investigation outcomes to product and model improvements Signal & Tool Utilization at Scale Operate across Plaid's fraud too ling — dashboards, alerting systems, network signals, and analytics platforms — to detect and validate anomalies Stress-test existing capabilities, identify systemic gaps, and define new detection primitives Proactively identify gaps in internal fraud tooling and automation, driving enhancements to improve efficiency and scale Product & Model Partnership Collaborate with Data Science, ML/AI, and Product teams to improve labeling, feature sets, evaluation frameworks, and model decay monitoring Surface data quality limitations and systematically formalize missing features Translate exploratory research into reusable feature pipelines, model inputs, or rule augmentations Participate in product discovery, roadmap planning, and post-launch evaluation to ensure fraud-awareness by design Deep...