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Dave is a fintech company building affordable, transparent financial products for underserved Americans. The company's flagship ExtraCash product provides up to $500 in liquidity within minutes, with new offerings including Buy Now Pay Later launching in 2026.
You will develop and deploy advanced machine learning solutions to detect fraud, protect members, and responsibly expand access to credit. This is a hands-on role requiring deep involvement across the full ML lifecycle: exploring datasets, developing predictive models, deploying solutions, evaluating performance, and continuously improving results.
Key responsibilities include:
- Building and deploying advanced fraud detection models to identify synthetic identities, first-party fraud, account takeover, and emerging fraud patterns
- Exploring large, diverse datasets, engineering predictive features, and evaluating modeling approaches to translate findings into production-ready solutions
- Owning the end-to-end ML lifecycle from research and prototyping through testing, deployment, monitoring, and continuous improvement
- Applying modern AI tools and techniques to accelerate research, coding, feature development, and experimentation while maintaining technical rigor
- Analyzing model performance, fraud trends, approval rates, and portfolio outcomes to improve detection accuracy and reduce losses
- Collaborating with Engineering, Product, and Risk Operations to integrate models into production systems and share technical expertise
Your models will directly reduce fraud losses, improve decision accuracy, and help members access financial tools they need.
REQUIREMENTS:
- 7+ years of hands-on machine learning experience with demonstrated track record of personally developing, deploying, and optimizing predictive models in production
- Deep fraud modeling expertise, including experience building ML solutions for identity fraud, synthetic identities, first-party fraud, and account takeover (ideally in fintech, consumer lending, or financial services)
- Advanced proficiency in Python and SQL with strong experience in feature engineering, model development, experimentation, performance evaluation, and production ML systems
- 4+ years of experience with ML frameworks and cloud technologies (TensorFlow, PyTorch, Spark, cloud-based ML infrastructure)
- Experience leveraging generative AI and AI-assisted tools across research, coding, analysis, experimentation, testing, and model development
- Strong analytical and business judgment to evaluate trade-offs across fraud prevention, approval rates, profitability, compliance, and member experience
- Proven ability to independently tackle complex ML problems, make sound technical decisions, and deliver measurable results
- Strong communication and collaboration skills, including ability to explain technical findings, partner across functions, and mentor fellow scientists
- Bachelor's degree in Computer Science, Mathematics, Statistics, Engineering, or related quantitative field (or equivalent practical experience)
- Bonus: Experience with graph databases, graph-based fraud detection, or network analytics
