Senior Data Scientist, Risk

San Francisco, California, United States

Full Time
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Posted 1 month ago

Brex is building the new global standard for financial services, starting with corporate cards. We are designing the product from first principles, enabling us to have unparalleled features and a seamless, modern experience for our customers. With backing from top venture firms and industry veterans such as Peter Thiel and Max Levchin, Brex is one of the fastest-growing startups to date, and we’re looking for someone to help scale the company with incredible people across the board. As a risk scientist, you will leverage your modeling and engineering experience to mitigate risk. Based in San Francisco, our team is committed to creating a driven and diverse company with ambitious people from wide-ranging backgrounds.


We are looking for people with a strong background in machine learning and engineering who can independently solve ill-defined data challenges from end to end. You should be technical, and be able to quickly pick-up new engineering skills needed. You should also be a strong communicator who is able to work with internal stakeholders to take your projects to completion.



  • Apply your expertise in quantitative analysis, software engineering to build scalable and robust real-time ML models. 
  • Collaborate with cross-functional teams to unravel complex problems by clearly formulating the problem statement, technical requirements, and present finding at all levels. 
  • Build internal tools or products to enable direct user interaction with the data sets and build ML systems with human-in-the-loop components. 
  • Design, execute, analyze, and interpret the results of experiments across our product.
  • Alongside business stakeholders and engineers, reconcile crucial data integrity issues.
  • Maintain a strong data driven culture within the company by interacting with diverse internal functions.

What We Value:

  • Substantial experience building mission critical systems with strong proficiencies in computer science fundamentals - Data structure / algorithm, design patterns, and testing principles. 
  • Strong interpersonal and communication skills and having worked with different business functions.
  • Desire to have very high impact through evaluation of commercial risk.
  • Ability to devise creative workarounds to difficult project roadblocks.
  • Ability to relentlessly chase down data integrity issues to their root cause.
  • Experience initiating and driving projects to completion with minimal guidance.
  • Thriving in a collaborative environment, filled with a diverse group of people with different expertise and backgrounds (we currently have around 30 nationalities represented, with more than ½ the company working in a country different from the one they grew up in).

Preferred Qualification:

  • Experience with credit underwriting or fraud models or domain knowledge in the financial space.

Data at Brex:

  • We work in an environment where it matters to make the right design decisions the first time, and as a result, take on less technical debt than other companies.
  • Product is a highly collaborative initiative across multiple teams. Data scientists are expected to understand the business and have input towards our long term vision.
  • We believe in two equal track career growths between senior individual contributors and managers. We want people to contribute where they feel most impactful.
  • We believe in small, accountable and autonomous teams of amazing people, eager to learn, teach and constantly improve our way of working.
  • People have a strong sense of ownership and accountability for what they’re building.  What we build today will be the foundation for dozens of other systems in the future.
  • We are very frank on discussing technical matters. If one disagrees with how things are being done, we encourage them to speak up and help us get to the truth faster. 


Does Brex sound like home? We'd love to meet you! Please share with us details of what you've worked on and what matters to you (personally and from a technical standpoint). Don't worry too much about your resumé. Be genuine, not official.

Job tags: Engineering Machine Learning ML