Data Scientist - Credit (Retros)

Plaid•New York, NY

About The Position

Plaid’s Data organization builds models and insights that help people better understand and grow their financial lives. The Credit team uses Plaid’s financial data to help lenders make informed decisions and build better credit experiences, partnering across data science, engineering, product, and Go-to-Market. As a Data Scientist on the Credit team, you will apply quantitative analysis and credit expertise to help lenders use cash flow insights. You will work across customer problems, credit policy, and data products, translating Plaid data into recommendations and insights that drive business impact and inform product direction. The role includes developing hands-on recommendations for incorporating cash flow insights into lending decisions, improving customer adoption through tailored analysis, and serving as a subject matter expert on credit insights and policy implementation. You will also inform Product and Engineering teams through clear analysis and presentations.

Requirements

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 expertise building and maintaining credit policies for lenders.
Knowledge of the lending lifecycle and how to optimize marketing, acquisition, customer management, and collections or recoveries.
Demonstrated ability to make data-driven decisions.

Nice To Haves

Experience building data pipelines and automating ETL processes with dbt or similar tools.

Responsibilities

Deep-dive into client model performance, triage issues, and identify opportunities to improve outcomes.
Analyze Plaid data to identify emerging credit risk signals and build and evaluate credit retrospectives.
Create tailored client recommendations based on customer data and Plaid insights.
Build and maintain data pipelines, including ETL processes.
Develop metrics, alerts, and dashboards to monitor production machine learning model performance.
Develop 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 customer needs.
Serve as a subject matter expert on credit insights and credit policy implementation, supporting Data Science, Engineering, and Go-to-Market partners.
Apply 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.