Data Scientist - Credit (Retros)

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, Seattle, Washington D.C., Raleigh, London, and Amsterdam.

About the TeamWe’re part of Plaid’s Data organization, building models and insights that help millions of people better understand and grow their financial lives. We use Plaid’s unique financial data to help lenders make more informed decisions and build better credit experiences.

We partner across data science, engineering, product, and Go-to-Market to turn complex data into practical customer recommendations and scalable solutions. We value technical rigor, creativity, user empathy, and teamwork.

About the RoleAs a Data Scientist on the Credit team, you’ll apply quantitative analysis and deep credit expertise to help lenders make better use of cash flow insights. You’ll work across customer problems, credit policy, and data products—translating Plaid’s data into recommendations and insights that drive business impact and inform product direction.

Your day-to-day responsibilities may include:

• Deep-dive into model performance for clients, triage issues, and identify opportunities to improve outcomes.

• Analyze Plaid’s unique data to identify emerging credit risk signals and build and evaluate credit retrospectives.

• Create tailored recommendations for clients based on their data and Plaid’s insights.

• Build and maintain data pipelines, including ETL processes using tools such as dbt.

• Develop metrics, alerts, and dashboards to monitor production machine learning model performance.

In this role, you will:

• Own the process of developing hands-on recommendations for incorporating cash flow insights into lending decisions, using best practices for account-level decisioning.

• Improve customer adoption of Plaid Insights through custom analysis tailored to each customer’s needs.

• Serve as a subject matter expert on credit insights and credit policy implementation, supporting Data Science, Engineering, and Go-to-Market partners.

• Apply your expertise in quantitative analysis, data mining, and data visualization to develop models and actionable insights.

• Identify useful signals that can inform machine learning models.

• Inform and influence Product and Engineering teams through clear analysis and presentations.

What Excites You- Applying data science and credit expertise to real-world lending decisions.

• Working with Plaid’s unique financial data to uncover emerging credit risk signals and create useful customer insights.

• Helping lenders make more informed decisions with cash flow data and account-level recommendations.

• Partnering across Data Science, Engineering, Product, and Go-to-Market to turn analysis into customer and product impact.

• Working on high-impact problems that can improve financial access and outcomes.

What Excites Us- 2–5 years of industry experience in a financial services Data Science role.

• Deep familiarity with SQL and Python.

• Deep understanding of credit risk and the development of underwriting models.

• Hands-on credit expertise building and maintaining credit policies for lenders.

• Knowledge of the lending lifecycle and how to optimize each step, including marketing, acquisition, customer management, and collections or recoveries.

• Demonstrated ability to make data-driven decis...