Data Scientist II, Credit

Data at Brex

Our Scientists and Engineers work together to make data—and insights derived from data—a core asset across Brex. But it's more than just crunching numbers. The Data team at Brex develops infrastructure, statistical models, and products using data. Our work is ingrained in Brex's decision-making process, the efficiency of our operations, our risk management policies, and the unparalleled experience we provide our customers.

What You’ll Do

As a Data Scientist II, Credit, you will partner closely with our Credit, Enterprise Risk, and Data Science teams to develop analytical frameworks that inform underwriting policies and credit risk strategies. Your work will directly support our Chief Risk Officer in executing a targeted loss rate while ensuring sustainable growth for our credit offerings. You will leverage data science techniques to enhance our credit risk assessment, optimize lending decisions, and refine our risk-based pricing models. Your insights will play a critical role in aligning risk management with business strategy and regulatory considerations.

The ideal candidate has experience in credit risk modeling, underwriting analytics, and quantitative risk assessment. They should have strong business acumen, deep familiarity with credit products, and the ability to communicate technical insights to non-technical stakeholders.

Where you’ll work

This role will be based in our San Francisco office. You must be willing to work in office at least 2 days per week on Wednesday and Thursday. Employees will be able to work remotely for up to 4 weeks per year.

Responsibilities:

• Develop analytical frameworks and risk models to inform underwriting decisions and credit policy optimization.
• Analyze loss rate trends, credit performance, and portfolio risk dynamics to drive strategy adjustments.
• Collaborate with Credit and Enterprise Risk teams to establish data-driven risk appetite frameworks and lending criteria.
• Work closely with our Credit Risk Machine Learning team to improve risk-scoring methodologies and loss forecasting models.
• Support pricing and limit-setting strategies through quantitative analysis of risk-adjusted returns.
• Design and evaluate tests to policy changes to measure the impact of credit risk strategies on financial performance.

Requirements:

• Master’s degree or Ph.D. in Statistics, Economics, Finance, Computer Science, or a related quantitative field.
• 3+ years of experience in credit risk analytics, underwriting strategy, or related data science roles in lending or financial services.
• Strong experience with SQL and Python (or R) for data analysis and modeling.
• Proficiency in credit risk modeling techniques, including logistic regression, scorecard development, and loss forecasting.
• Familiarity with credit bureau data, alternative data sources, and regulatory risk considerations.
• Ability to synthesize complex analyses into actionable business insights and communicate findings to stakeholders.
• Experience with experimentation and causal inference methodologies.

Bonus Points:

• Experience working in B2B SaaS or fintech, particularly in a lending environment.
• Familiarity with risk-based pricing models and financial modeling techniques.
• Experience working with credit decisioning systems and automated underwriting workflows.
• Knowledge of regulatory frameworks such as CECL, Basel II/III, or stress testing methodologies.

This role offers a unique opportunity to shape the future of credit risk strategy at Brex while working with a world-class data and risk management team. If you are passionate about using data science to drive better lending decisions, we’d love to hear from you!

Compensation

The expected salary range for this role is $152,000 - $190,000. However, the starting base pay will depend on a number of factors including the candidate’s location, skills, experience, market demands, and internal pay parity. Depending on the position offered, equity and other forms of compensation may be provided as part of a total compensation package.