Senior Data Scientist - Credit

Plaid•San Francisco, CA

About The Position

We build credit models, retros, and decisioning insights that help lenders make better underwriting decisions using Plaid’s unique data. We partner closely across data science, engineering, product, and go-to-market teams to turn cash flow and account-level signals into clear, actionable recommendations for customers. We care deeply about combining technical rigor, business impact, and customer empathy to improve how credit decisions are made. You’ll be an experienced data scientist focused on credit analytics and underwriting insights at Plaid. You will work across modeling, retros, customer analysis, and product feedback loops to help lenders better understand and adopt Plaid’s credit insights. This role is a strong fit for someone who enjoys applying quantitative analysis to real lending decisions, partnering cross-functionally, and translating complex data into recommendations that drive customer and business impact.

Requirements

4+ years of experience in a data science, credit risk, or analytical role within financial services or fintech.
Deep familiarity with SQL and Python.
Strong understanding of credit risk and underwriting model development.
Hands-on experience building or maintaining credit policies for lenders.
Knowledge of the lending lifecycle and how to optimize across acquisition, underwriting, customer management, and collections or recoveries.
Demonstrated ability to make data-driven decisions and communicate recommendations clearly to cross-functional stakeholders.
Experience working directly with lenders or credit products and translating analysis into customer-facing recommendations.

Nice To Haves

Familiarity with model monitoring, automation, modern data tooling, or working with large-scale financial datasets.

Responsibilities

Own hands-on recommendations for incorporating cash flow insights into lending decisions, including best practices for account-level decisioning.
Improve customer adoption of Plaid insights data through custom analysis tailored to specific customer needs.
Act as a subject matter expert for credit insights and credit policy implementation across data science, engineering, and go-to-market partners.
Deep dive into model and retro performance to identify opportunities for improvement and emerging credit risk vectors.
Build and work with credit retros to evaluate outcomes and inform customer recommendations.
Develop features, analyses, metrics, and monitoring approaches that improve risk models and insight delivery.
Partner with engineering to improve workflows, automate processes where helpful, and raise the quality of data pipelines and outputs.
Inform and influence product and engineering roadmaps through clear analysis, presentations, and stakeholder communication.