Senior Data Scientist, Finance

About the position Brex is the AI-powered spend platform. We help companies spend with confidence with integrated corporate cards, banking, and global payments, plus intuitive software for travel and expenses. Tens of thousands of companies from startups to enterprises — including DoorDash, Flexport, and Compass — use Brex to proactively control spend, reduce costs, and increase efficiency on a global scale. Working at Brex allows you to push your limits, challenge the status quo, and collaborate with some of the brightest minds in the industry. We’re committed to building a diverse team and inclusive culture and believe your potential should only be limited by how big you can dream. We make this a reality by empowering you with the tools, resources, and support you need to grow your career. 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 Working closely with Finance leadership, you’ll develop a financial forecasting system using advanced data science techniques. You’ll analyze key business drivers to inform strategic financial decisions and enhance forecast precision. The ideal candidate has expertise in predictive modeling, causal inference, and experience collaborating with Corporate and Strategic Finance teams, particularly in forecasting within a blended revenue model that includes both recurring and consumption-based components. Where you’ll work This role will be based in our San Francisco office. We are a hybrid environment that combines the energy and connections of being in the office with the benefits and flexibility of working from home. We currently require a minimum of two coordinated days in the office per week, Wednesday and Thursday. Starting February 2, 2026, we will require three days per week in office - Monday, Wednesday and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities

• Design and build a new top-line revenue and other financial forecasts using predictive modeling and other advanced data science techniques.
• Collaborate with Finance to integrate predictive insights into existing forecasting processes and refine key assumptions.
• Partner with Finance to analyze financial performance and uncover key drivers using causal inference, anomaly detection, and exploratory data analysis.
• Design and implement scalable data pipelines to support financial reporting and forecasting in collaboration with Data Engineering.
• Mentor junior data scientists and finance analysts to foster a culture of data-driven decision-making.
• Communicate findings and recommendations clearly to both technical and non-technical audiences.

Requirements

• Master's degree or Ph.D. in Finance, Statistics, Economics or a related quantitative field.
• 5+ years of experience in a data science or related role supporting finance teams.
• Expertise in predictive modeling, causal inference, and time series forecasting.
• Knowledge of structural finance models, financial planning and analysis (FP&A) workflows and reporting, plus experience working with key performance indicators like LTV, CAC, and ARR.
• Proficiency in SQL and Python (or R) for data analysis and modeling.
• Ability to translate complex analyses into strategic recommendations for Finance and business leadership.
• Familiarity with BI tools (e.g., Tableau, Looker) and financial data sources.
• Excellent problem-solving skills and the ability to work independently in a fast-paced environment.
• Strong communication skills, with the ability to work cross-functionally.

Nice-to-haves

• Experience building and maintaining revenue prediction models with demonstrated accuracy in production environments.
• Experience working in businesses with blended revenue models that include both recurring and consumption-based components.

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