Job Description:
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. Plaid’s Payment Data team builds models, services, and platforms that improve how millions of users move money through ACH. We’re looking for data scientists to apply best-in-class methodologies to develop new products, enhance existing systems, and generate insights that power data-driven decision-making. You’ll be a Data Scientist as a part of the Data org, tackling high-impact analytics and machine learning challenges that directly shape business outcomes. In this role, you’ll use data to drive strategic decisions, inform production direction, and evaluate and improve ML systems. You’ll partner closely with clients both pre- and post-sales to assess product performance, conduct retrospective analyses, and craft risk mitigation strategies that maximize impact. Day to day, you’ll deep dive into model performance, triage issues, and identify opportunities for improvement across ML risk systems. You’ll develop new features to strengthen model performance, build scalable data pipelines using tools like dbt to automate ETL processes, and design metrics, alerts, and dashboards to monitor production models. This is an opportunity to combine analytical rigor, technical depth, and client collaboration to drive measurable results.
