Senior Quantitative Financial Analyst

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

We're looking for a Senior Quantitative Financial Analyst to lead revenue forecasting at Brex by building models that shape our financial strategy and planning. This role partners directly with Corporate Finance leadership and requires both statistical depth (e.g., machine learning, causal inference) and fluency in business and financial modeling (e.g., scenario and revenue mix modeling). You'll serve as a bridge between Data Science and Finance by architecting predictive systems that guide planning, investment, and growth. The ideal candidate has expertise in predictive modeling, causal inference methods, and experience collaborating with Corporate and Strategic Finance teams, particularly in revenue forecasting within blended models that include both recurring and consumption-based components.

Where you’ll work

This role will be based in our New York 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:

• Design and build scalable top-line revenue prediction systems using machine learning, time series modeling, and advanced statistical techniques.
• Collaborate with Finance to integrate predictive insights into strategic planning processes and validate key business assumptions.
• Partner with Finance to analyze business performance and identify causal drivers using methods like difference-in-differences, synthetic controls, regression discontinuity, and causal ML approaches.
• Design and implement robust data pipelines and modeling infrastructure to support enterprise-scale revenue 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 strategic recommendations clearly to executive leadership and cross-functional teams.

Requirements:

• Master's degree or Ph.D. in Statistics, Economics, Finance, or a related quantitative field.
• 5+ years of experience in a data science role building predictive models for business-critical applications.
• Expertise in machine learning, causal inference methodologies, and time series forecasting techniques.
• Knowledge of business metrics and KPIs like LTV, CAC, and ARR, plus experience with revenue modeling and business performance analysis.
• Proficiency in SQL and Python (or R) for data analysis and modeling.
• Ability to translate complex statistical analyses into strategic recommendations for executive decision-making.
• Familiarity with BI tools (e.g., Tableau, Looker) and enterprise data infrastructure.
• 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.

Bonus Points:

• 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.

Compensation

The expected salary range for this role is $192,000 - $240,000. 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.