Staff Data Scientist, ML (Credit Risk)

September 19, 2026

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About the position

Join us in building the future of finance. Our mission is to democratize finance for all. An estimated \$124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading. About the team + role We are building an elite team, applying frontier technologies to the world's biggest financial problems. We're looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn't a place for complacency, it's where ambitious people do the best work of their careers. We're a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards. The Credit Card business team's mission is to shape Robinhood’s vision in the credit and banking space by delivering smart, customer-focused financial solutions. Our team is dedicated to reshaping the credit landscape and redefining the way people interact with financial services daily. We leverage cutting-edge analytical tools and diverse datasets to build deep understandings of consumer credit behaviors. We aim to make financial services accessible to everyone, building programs that support Robinhood’s broader goals! As a Staff Data Scientist, you will build credit risk models that allow us to better serve our customers and make responsible lending decisions. Credit models are at the heart of all lending decisions. These models will drive decisions ranging from approve/decline, line assignment at origination and future credit limit increases. You will leverage traditional and non-traditional data sources to build highly predictive risk models. You will own the full lifecycle of model development from data prep, model building to model deployment. This role is based in our Bellevue, WA, Menlo Park, CA, or Washington, DC office(s), with in-person attendance expected at least 3 days per week. At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.

Responsibilities

Build credit risk models for customer acquisitions (credit approval and credit limit assignment) and customer management
Collaborate with credit analysts and product managers to understand the business problem and deliver appropriate models to solve them
Create rich datasets leveraging both traditional and non-traditional data sources.
Work on innovative tools to generate powerful features that boost models
Deploy, maintain and monitor models in production
Communicate with senior stakeholders in credit, product and engineering to deliver key results and findings

Requirements

7+ years of experience in data science and credit modeling
Strong proficiency in SQL and Python for data analysis and modeling
Experience designing experiments and interpreting results to guide product decisions
Domain knowledge and expertise in traditional credit data.
Communicate clearly with both technical and non-technical partners

Nice-to-haves

Experience with non traditional credit data (such as cash flow) is a plus

Benefits

Performance driven compensation with multipliers for outsized impact, bonus programs, equity ownership, and 401(k) matching
Top Tier benefits to fuel your work, including 100% paid health insurance for employees with 90% coverage for dependents
Access to the best AI tools on the market and continuous AI skill-building for every employee, technical or not
Lifestyle wallet - a highly flexible benefits spending account for wellness, learning, and more
Employer-paid life & disability insurance, fertility benefits, and mental health benefits
Time off to recharge including company holidays, paid time off, sick time, parental leave, and more!
Exceptional office experience with catered meals, events, and comfortable workspaces.

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