Lead Machine Learning Scientist

About the role

DAVE VS. GOLIATH. WE’RE DAVE.

Dave is a financial app on a mission to build products that level the financial playing field. It is redefining the financial landscape by leveraging technology to create an affordable, transparent, and user-centric access to liquidity for millions of Americans. As a leading innovator in the U.S. financial services sector, Dave’s digital financial platform offers products designed to meet the credit needs of those underserved by traditional financial institutions. Dave’s offerings include its flagship ExtraCash product, providing members up to $500 within minutes. The company is on track to launch several new product offerings in 2026, including a Buy Now Pay Later (BNPL) option.

Dave is focused on serving Americans who are financially vulnerable or living paycheck to paycheck. Dave is leading the charge in creating a new era of credit products that prioritizes speed, affordability, and accessibility, making it the go-to financial partner for those who need it most.

THE OPPORTUNITY

We're looking for a Lead Machine Learning Scientist to develop and deploy advanced machine learning solutions that help Dave detect fraud, protect our members, and responsibly expand access to credit.

This is a deeply hands-on role for someone who loves solving complex problems through data, experimentation, and modeling. You'll work across the full machine learning lifecycle, from exploring large datasets and developing predictive models to deploying solutions, evaluating performance, and continuously improving results.

You'll have meaningful ownership of technically challenging problems, with opportunities to influence how we approach fraud detection and risk decisioning. You'll collaborate closely with Product, Engineering, and Risk Operations while remaining actively involved in the modeling and technical execution that drives our business forward.

WHAT YOU'LL BUILD & OWN

- Build and deploy advanced fraud detection models. Personally develop, train, validate, and optimize machine learning models that identify synthetic identities, first-party fraud, account takeover, and emerging fraud patterns.

- Solve complex problems through data and experimentation. Explore large, diverse datasets, engineer predictive features, evaluate modeling approaches, and translate findings into production-ready fraud solutions.

- Own the end-to-end ML lifecycle. Take models from initial research and prototyping through testing, deployment, monitoring, and continuous improvement, ensuring solutions deliver measurable business results.

- Apply AI to accelerate machine learning development. Use modern AI tools and techniques to improve research, coding, feature development, experimentation, validation, and modeling workflows while maintaining strong technical rigor.

- Optimize fraud decisioning through measurable results. Analyze model performance, fraud trends, approval rates, and portfolio outcomes to identify opportunities to improve detection accuracy, reduce losses, and support responsible growth.