1. What is a Data Scientist at Brex?

As a Data Scientist at Brex, you are stepping into a pivotal role at the forefront of the AI-powered spend platform. Brex helps tens of thousands of companies—from fast-growing startups to global enterprises like DoorDash and Flexport—spend with confidence. The Data team here does not just build dashboards; we turn information into a strategic advantage. You will treat data as a product, and your ownership of that product will run deep.

In this role, you are embedded directly within cross-functional units such as Product, Engineering, Risk, Operations, and Customer Success. Your primary objective is to understand the complex interactions between product engagement, customer retention, and overall growth. Whether you are optimizing user workflows for our travel and expense software or uncovering insights that increase long-term customer value, your work will directly shape high-impact strategic decisions.

Working at Brex allows you to push your limits and challenge the status quo. If you are excited by complexity and want your analytical work to drive sustainable revenue growth and operational efficiency at a massive scale, this is exactly where you belong. Expect a fast-paced environment where your product intuition is valued just as highly as your technical rigor.

2. Common Interview Questions

3. Getting Ready for Your Interviews

Preparing for a Data Scientist interview at Brex requires a balanced approach. We do not just look for people who can write flawless code; we look for strategic thinkers who can translate ambiguous business problems into actionable data models.

Here are the key evaluation criteria you will be measured against:

Product Intuition & Business Acumen – You must demonstrate a deep understanding of how users interact with financial products. Interviewers will evaluate your ability to tie data metrics directly to customer retention, engagement, and revenue growth. You can show strength here by always framing your technical solutions within the context of the broader business impact.

Technical Rigor & Execution – This covers your proficiency in SQL, Python or R, and applied statistics. We evaluate your ability to manipulate complex datasets, design robust experiments, and build scalable data models. Strong candidates write clean, efficient code and can explain the statistical foundations behind their chosen methodologies.

Problem Solving & Ambiguity Navigation – At Brex, you will rarely be handed a perfectly scoped problem. Interviewers will assess how you break down open-ended questions, state your assumptions, and structure a logical path to a solution. You can excel here by proactively communicating your thought process and asking clarifying questions.

Ownership & Collaboration – Because our data scientists are embedded across various teams, your ability to communicate complex insights to non-technical stakeholders is critical. We look for candidates who take end-to-end ownership of their work and can influence product strategies through clear, compelling data storytelling.

4. Interview Process Overview

The interview process for a Data Scientist at Brex is designed to be rigorous, fair, and highly reflective of the actual day-to-day work. It typically begins with an initial recruiter screen to align on your background, career goals, and fundamental fit for the role. This is followed by a technical screen, which usually involves live coding in SQL and Python to assess your baseline data manipulation and analytical problem-solving skills.

If you advance, you will move to the core of the evaluation: a comprehensive onsite loop (conducted remotely). This loop consists of several distinct rounds focusing on product sense, applied statistics and experimentation, advanced technical execution, and behavioral alignment. Brex places a heavy emphasis on how you approach product analytics, meaning you will face scenario-based questions that mirror real challenges our Product and Engineering teams tackle daily.

What sets the Brex process apart is the focus on "data as a product." You will not just be asked to compute a p-value or write a window function; you will be asked why that metric matters to our customers and how it improves the Brex platform.

This visual timeline outlines the typical stages of your interview journey, from the initial recruiter screen through the final onsite rounds. Use this roadmap to structure your preparation, ensuring you allocate enough time to practice both your technical coding skills and your high-level product strategy frameworks. Keep in mind that specific rounds may vary slightly depending on whether you are interviewing for a Product Analytics or Finance-focused data science pod.

5. Deep Dive into Evaluation Areas

To succeed in the Brex interview process, you need to be exceptionally prepared across several core competencies. Below is a detailed breakdown of the primary evaluation areas.

Product Analytics & Metrics Design

Understanding the "why" behind user behavior is crucial. This area tests your ability to define success metrics, identify root causes for metric shifts, and propose product improvements based on data. Strong performance means you do not just list standard metrics; you tailor them specifically to Brex's B2B financial products.

Be ready to go over:

• Metric formulation – Defining primary, secondary, and guardrail metrics for a new feature launch.
• Root cause analysis – Investigating a sudden drop in corporate card usage or an increase in expense reporting drop-offs.
• User lifecycle analysis – Measuring activation, engagement, and churn in a SaaS or fintech context.
• Advanced concepts (less common) – Cannibalization effects, network effects in B2B payments, and sophisticated LTV/CAC modeling.

Example questions or scenarios:

• "If the activation rate for our new integrated travel booking software dropped by 15% week-over-week, how would you investigate the cause?"
• "How would you design a dashboard to monitor the health of our global payments product?"
• "What metrics would you use to evaluate whether a new automated receipt-matching feature is successful?"

Applied Statistics & Experimentation

Because Data Scientist roles at Brex heavily influence product strategy, you must be highly proficient in designing and evaluating A/B tests. Interviewers want to see that you understand the mathematical principles behind experimentation and can identify when standard A/B testing is not appropriate.

Be ready to go over:

• Experiment design – Choosing randomization units, determining sample sizes, and defining minimum detectable effects (MDE).
• Statistical concepts – P-values, confidence intervals, statistical power, and Type I/Type II errors.
• Handling practical challenges – Dealing with novelty effects, day-of-week seasonality, and non-normal distributions.
• Advanced concepts (less common) – Causal inference, synthetic control methods, and multi-armed bandits.

Example questions or scenarios:

• "Design an experiment to test a new onboarding flow for enterprise customers. What are the potential pitfalls?"
• "How would you analyze an A/B test where the sample size is extremely small due to targeting a niche segment of startup founders?"
• "Explain how you would handle interference or network effects if we tested a new referral program."

Technical Execution (SQL & Python/R)

You cannot treat data as a product if you cannot extract and manipulate it efficiently. This area evaluates your hands-on coding ability. Strong candidates write optimized, readable code and are comfortable navigating complex, messy datasets.

Be ready to go over:

• Advanced SQL – Complex joins, window functions, CTEs (Common Table Expressions), and aggregations.
• Data manipulation in Python/R – Using Pandas or Dplyr to clean, merge, and transform data.
• Edge cases – Handling null values, duplicates, and data anomalies gracefully.

Example questions or scenarios:

• "Write a SQL query to find the top 3 spending categories for each company in the last 30 days, accounting for potential refunds."
• "Given a raw dataset of user login events and transaction timestamps, write a Python script to calculate the average time to first transaction."

Behavioral & Cross-Functional Collaboration

Brex values a diverse team and an inclusive culture where ownership runs deep. This area evaluates how you work with Product Managers, Engineers, and business leaders. Strong candidates demonstrate a history of taking initiative, resolving conflicts through data, and communicating complex technical concepts to non-technical audiences.

Be ready to go over:

• Stakeholder management – Pushing back on flawed product assumptions using data.
• Navigating ambiguity – Taking a vague business question and turning it into a concrete analytical project.
• Impact and leadership – Examples of when your insights directly changed a product roadmap or saved the company money.

Example questions or scenarios:

• "Tell me about a time you had to convince a skeptical Product Manager to change their strategy based on your data."
• "Describe a situation where you had to deliver an analysis with incomplete or messy data."