Senior Machine Learning Engineer - Finance Management

We build simple yet innovative consumer products and developer APIs that shape how everybody interacts with money and the financial system. 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 Earned Wage Access (EWA) team is building Plaid’s industry-leading solution for assessing repayment risk in EWA. By leveraging Plaid’s unique data and modeling capabilities, the team aims to deliver the most accurate and scalable EWA score in the market—surpassing existing offerings and setting a new standard for how partners evaluate and manage risk in this emerging space. As a Data Scientist on the EWA team, you’ll work across the full product lifecycle—from pre-launch evaluation through post-launch retrospectives—to analyze performance, assess repayment patterns, and design strategies that optimize outcomes for partners and end users. You’ll refine Plaid’s EWA risk models, identifying opportunities to improve predictive accuracy and uncover new behavioral signals unique to EWA users. In this role, you’ll also develop new features to strengthen risk prediction, build scalable data pipelines with tools like dbt to automate ETL processes, and create metrics, alerts, and dashboards to continuously monitor production model performance.