> Markdown version of [/jobs/ext/3851246-lead-machine-learning-scientist](https://www.wearedevelopers.com/jobs/ext/3851246-lead-machine-learning-scientist). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Lead Machine Learning Scientist

- **Company:** Dave, Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $174,000.0 - $224,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Network Analysis, Cloud Computing, Fraud Prevention and Detection, Graph Database, Python (Programming Language), Machine Learning, Tensorflow, SQL Databases, Feature Engineering, Pytorch, Apache Spark, Information Technology, Machine Learning Operations
- **Published:** October 21, 2026
- **Apply:** https://www.builtincolorado.com/job/lead-machine-learning-scientist/11780672?handler=ApplyRedirect

## About the Role

* 7+ years of hands-on machine learning experience, with a demonstrated track record of personally developing, deploying, and optimizing predictive models in production environments.
 * Deep fraud modeling expertise, including experience building ML solutions to detect identity fraud, synthetic identities, first-party fraud, and account takeover, ideally within 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, including tools such as TensorFlow, PyTorch, Spark, and cloud-based ML infrastructure.
 * Experience leveraging generative AI and AI-assisted tools across research, coding, analysis, experimentation, testing, and model development, with sound judgment in validating outputs.
 * Strong analytical and business judgment, with the ability to evaluate trade-offs across fraud prevention, approval rates, profitability, compliance, and member experience.
 * Proven end-to-end execution, with the ability to independently tackle complex ML problems, make sound technical decisions, and deliver measurable results.
 * Strong communication and collaboration skills, including the ability to explain technical findings, partner across functions, and mentor fellow scientists through hands-on expertise.
 * Bachelor's degree in Computer Science, Mathematics, Statistics, Engineering, or a related quantitative field, or equivalent practical experience.

Bonus points: Experience with graph databases, graph-based fraud detection, or network analytics.

What Makes Someone Successful Here

You enjoy getting into the details. You're curious about data, thoughtful in your modeling decisions, and motivated by seeing your work make a measurable difference. You take ownership from problem definition through production, balancing technical rigor with practical business outcomes. You care about building solutions that work today and continue delivering value as Dave grows.

You also recognize that great models don't exist in isolation. You collaborate openly, welcome feedback, and understand how your work connects to broader product and business decisions. When new information challenges your assumptions, you're willing to rethink your approach and find a better solution.

## Description

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.
 * Collaborate and contribute technical expertise. Partner with Engineering, Product, and Risk Operations to integrate models into production systems, solve implementation challenges, and share technical knowledge that strengthens the broader team.

The Impact You'll Make

Your models will help Dave make smarter, more responsible fraud and credit decisions. You'll directly contribute to reducing fraud losses, improving decision accuracy, and helping more members access the financial tools they need.