Lead Data Scientist
Brigit
Description
Hi, we're Brigit! A holistic financial health company helping everyday Americans build a brighter financial future. With a business model that is aligned with our customers, we create transparent, fair, and simple financial products that put money back in the hands of our members, help them spend wisely, avoid unfair fees and build their credit quickly. If autonomy, ownership, and having meaningful input at the company you work for is important to you, come join our growing team!
Brigit is doing innovative and exciting work, but donât just take our word for it, our work is being recognized by others:
Built In's 2026 Best Midsize Companies to Work For in New York City
Built Inâs 2024 & 2025 Best Startups to Work For In the U.S.
Built Inâs 2023 - 2025 Best Startups to Work For In New York City
Role Overview:
At Brigit weâre focused on giving the 100M Americans who live paycheck to paycheck access to more affordable financial services and getting them on the path to better financial wellness. As a Data Scientist on our team youâll be responsible for building, improving and maintaining the key ML models that enable our services. The key problem spaces we are focused on are:
Identifying credit risk of customers so that we can help more people in need when they need it
Identify fraud early, so we can focus on customers that rely on us for support
Optimizing our transaction timing so we arenât causing our customers to overdraw their accounts.
We have access to rich, structured data that we can use to derive insights and build complex models. In addition to supporting new use cases when needed, you will also have the opportunity to help us stand-up best practices and collaborate across Data Science. Analytics, Product and Engineering teams.
What youâll be doing:
Build, test and roll out new underwriting and risk models to better predict risk
Youâll have ownership of the full modeling lifecycle from conception through to production and realize every bit of impact along the way.
Build, test and roll out new or improved models related to fraud and payments.
Contribute to building best practices for feature development, model training and model testing and monitoring.
Analyze how our customer base is shifting as we grow and pinpoint areas we can improve.
Help our existing engineering and business teams achieve our cross-functional goals.
Mentor more junior data scientists or aspiring data scientists across the data team.
What you have:
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About Brigit
Company scraped from remoteok
Job Stats
Hiring Across Borders?
Interview Prep Guide
Preparation Strategy
To prepare for this role, focus on reviewing machine learning concepts, particularly those related to credit risk assessment and fraud detection. Practice explaining complex technical ideas in simple terms and prepare examples of your past experiences working in data science. Additionally, research the company and its mission to demonstrate your understanding of their specific challenges and goals. Review common data science interview questions and practice whiteboarding exercises to improve your problem-solving skills.
Likely Interview Rounds
- 1. Technical~60 min
What to prep: Review machine learning concepts, particularly those related to credit risk assessment and fraud detection. Practice explaining complex technical ideas in simple terms.
- How would you approach building a credit risk model for our customer base?
- What techniques would you use to identify and prevent fraud in our system?
- How do you optimize transaction timing to prevent overdrafts?
- 2. Behavioral~60 min
What to prep: Prepare examples of your past experiences working in data science, focusing on collaboration, problem-solving, and communication skills.
- Can you describe a time when you had to collaborate with cross-functional teams to implement a data-driven solution?
- How do you handle conflicting priorities and tight deadlines in your work?
- Tell me about a project where you had to derive insights from complex data sets.
Most Likely Questions
- What do you know about our company and our mission?
- How do you stay current with new developments in data science and machine learning?
- Can you walk me through your process for building and deploying a machine learning model?
Common Pitfalls
- Lack of understanding of the company's specific challenges and goals
- Inability to communicate complex technical ideas in simple terms
- Insufficient preparation for behavioral questions
Free Prep Resources
- • LeetCode
- • Kaggle
- • System Design Primer (GitHub: donnemartin)