Lead Machine Learning Scientist

You will develop, train, validate, optimize, and deploy advanced machine learning models for fraud detection. You will own the full ML lifecycle, from data exploration and feature engineering through testing, monitoring, and continuous improvement. You will evaluate fraud trends and model performance, improve decisioning outcomes, and partner with Engineering, Product, and Risk Operations to integrate production solutions.

Responsibilities

• Build, train, validate, optimize, and deploy fraud detection models
• Explore large datasets and engineer predictive features
• Evaluate modeling approaches and translate findings into production-ready fraud solutions
• Own the ML lifecycle from research and prototyping through deployment, monitoring, and continuous improvement
• Use AI tools to improve research, coding, experimentation, validation, and modeling workflows
• Analyze model performance, fraud trends, approval rates, and portfolio outcomes
• Improve detection accuracy, reduce losses, and support responsible growth
• Partner with Engineering, Product, and Risk Operations to integrate models into production systems
• Share technical knowledge and mentor fellow scientists

Requirements

• 7+ years of hands-on machine learning experience developing, deploying, and optimizing predictive production models
• Fraud modeling experience covering identity fraud, synthetic identities, first-party fraud, and account takeover
• Advanced proficiency in Python and SQL
• Experience with feature engineering, model development, experimentation, performance evaluation, and production ML systems
• 4+ years of experience with ML frameworks and cloud technologies, including TensorFlow, PyTorch, Spark, and cloud-based ML infrastructure
• Experience using generative AI and AI-assisted tools for research, coding, analysis, experimentation, testing, and model development
• Ability to assess trade-offs across fraud prevention, approval rates, profitability, compliance, and member experience
• End-to-end execution of complex ML problems and measurable results
• Communication, collaboration, and mentoring skills
• Bachelor's degree in a quantitative field or equivalent practical experience

Benefits

• Flexible hours
• Virtual-first work culture
• Home office stipend
• Medical, dental, and vision insurance
• Paid parental and caregiver leave
• 401(k) matching contributions
• Financial advisor and financial wellness support
• Flexible PTO
• Company holidays, including Juneteenth and Winter Break
• All-company in-person and virtual events