Staff Data Scientist, ML (Credit Risk)

Robinhood; New York, Washington, Menlo Park (NY, District of Columbia, CA); Hybrid; USD 217,000 - 255,000; Full time

Benefits offered by this job

100% paid health insurance for employees

Bonus programs and equity ownership

Flexible benefits spending account

Job summary

Robinhood is on the lookout for a Staff Data Scientist to develop credit risk models that enhance customer acquisition and management strategies. This pivotal role involves collaborating with credit analysts and product managers to deliver impactful models that drive decisions in lending.

The ideal candidate will possess 7+ years of experience in data science, particularly in credit modeling, and have strong skills in SQL and Python. Join us to accelerate innovation in finance with a focus on responsible lending practices.

We offer competitive compensation, including bonuses and robust benefits, while our office experience is designed to foster a high-performing team culture.

Qualifications

• You have 7+ years of experience in data science and credit modeling.
• Strong proficiency in SQL and Python for data analysis.
• Experience designing experiments and interpreting results.

Responsibilities

• Build credit risk models for customer acquisitions and management.
• Collaborate with analysts and managers to solve business problems.
• Deploy, maintain and monitor models in production.

Skills

Data science

Credit modeling

SQL

Python

Data analysis

Education

7+ years in data science

Tools

Analytical tools

Job description

Menlo Park, CA; New York, NY; Washington, DC

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.

What you’ll do

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.

What you bring

You have 7+ years of experience in data science and credit modeling.

You have strong proficiency in SQL and Python for data analysis and modeling.

You have experience designing experiments and interpreting results to guide product decisions.

You have domain knowledge and expertise in traditional credit data. Experience with non‑traditional credit data (such as cash flow) is a plus.

You communicate clearly with both technical and non‑technical partners.

What we offer

Challenging, high‑impact work to grow your career.

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.

In addition to the base pay range listed below, this role is also eligible for bonus opportunities + equity + benefits.

Base pay for the successful applicant will depend on a variety of job‑related factors, which may include education, training, experience, location, business needs, or market demands. The expected base pay range for this role is based on the location where the work will be performed and is aligned to one of 3 compensation zones. For other locations not listed, compensation can be discussed with your recruiter during the interview process.

Base Pay Range:

Zone 1 (Menlo Park, CA; New York, NY; Bellevue, WA; Washington, DC)

$217,000 - $255,000 USD

Zone 2 (Denver, CO; Westlake, TX; Chicago, IL)

$191,000 - $224,000 USD

Zone 3 (Lake Mary, FL; Clearwater, FL; Gainesville, FL)

$170,000 - $199,000 USD

Click here to learn more about our Total Rewards, which vary by region and entity.

If our mission energizes you and you’re ready to build the future of finance, we look forward to seeing your application.

Equal Employment Opportunity Information

At Robinhood, we care deeply about diverse representation in our workforce as it supports our mission to democratize finance for all. In support of this goal, we encourage applicants to voluntarily identify demographic information. This information helps us to continue building a more inclusive workplace and to ensure effective recruiting programs that are inclusive of individuals across all backgrounds.

Self‑identifying in this section is completely voluntary and if you choose not to provide any information, please select the "I don't wish to answer" option under that question. Whatever your decision, data that you provide in this section will not be considered in the hiring process or thereafter. Individuals seeking employment at Robinhood are considered without regard to race, color, religion, national origin, age, sex, sexual orientation, marital status, ancestry, physical or mental disability, neurodivergence, veteran status, gender identity or expression, or any other characteristic protected by law.

You are being given the opportunity to provide the following information in order to help us comply with federal and state Equal Employment Opportunity record‑keeping, reporting, and other legal requirements, and to be used in our efforts to recruit a diverse workforce. Any information that you do provide will be recorded and maintained in a confidential manner. For more information on EEOC definitions, please reference this document .

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