Senior Data Scientist

Data at Brex

The Data team turns information into advantage. We design infrastructure, develop models, and deliver insights that power smarter decisions across Brex. Our scientists and engineers are embedded across product, engineering, risk, operations, and business teams — building data products, improving customer experiences, and shaping high-impact strategies. We treat data as a product, and ownership runs deep. If you're excited by complexity and want your work to drive decisions at scale, this is your team.

What You’ll Do
As a Senior Data Scientist focused on Product Analytics, you will work closely with Product, Engineering, and Customer Success teams to understand the interactions between product engagement, customer retention, and overall customer growth. Your work will play a critical role in shaping strategic product decisions by uncovering insights into how customers interact with Brex’s platform, identifying opportunities to refine the product experience, optimize user workflows, and introduce impactful changes that increase long-term customer value and drive sustainable revenue growth.

The ideal candidate has significant experience leveraging product intuition and advanced analytics to understand customer behavior, measure product impact on revenue retention and growth, and derive insights through causal inference and predictive modeling. They should also have a strong ability to influence product and business strategy by translating data insights into actionable recommendations.

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 the office - Monday, Wednesday, and Thursday. As a perk, we also have up to four weeks per year of fully remote work!

Responsibilities:

• Lead the development and application of data-driven methodologies to analyze customer behavior, retention trends, and product adoption, focusing on how product changes impact long-term customer value.
• Build, implement, and evaluate predictive models and experimentation strategies to measure and optimize the effect of product changes on customer growth and engagement.
• Act as a thought partner to Product, Engineering, and Customer Success leadership, identifying and prioritizing opportunities for improving customer engagement and long-term retention.
• Collaborate with Data Engineering to ensure scalable, reliable data pipelines that support product analytics and reporting.
• Communicate findings, insights, and strategic recommendations clearly and persuasively to executives, product leaders, and cross-functional stakeholders.
• Mentor junior data scientists and contribute to building best practices across the Data Science

Requirements:

• Master’s degree or Ph.D. in Statistics, Economics, Applied Mathematics, or a related quantitative field.
• 5+ years of experience in a data science, analytics, or related role with a focus on product analytics.
• Strong expertise with SQL and Python (or R) for data analysis and modeling.
• Deep proficiency in causal inference and predictive modeling.
• Demonstrated ability to influence senior stakeholders and translate complex data insights into strategic business recommendations.
• Familiarity with BI tools (e.g., Tableau, Looker) and data visualization best practices.
• Excellent problem-solving skills and ability to thrive in a fast-paced, cross-functional environment.

Bonus Points:

• Experience working in B2B SaaS or fintech.
• Experience in developing frameworks to measure feature adoption and identifying product opportunities that drive customer lifetime value.
• Familiarity with product analytics tools (e.g., Amplitude, StatSig).

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.