Own the ML model lifecycle from requirements to deployment with ML engineers
Translate fraud patterns into ML solutions; define scope and success criteria
Design and maintain feature engineering pipelines for model development
Monitor production model quality, data drift, and retraining needs
Partner with leadership, GTM, fraud ops, product, and eng teams on fraud strategies
Champion continuous learning and collaboration across data science teams

🎯 Requirements

Bachelor’s degree or higher in quantitative field
3+ years building and deploying ML systems in production
Strong Python and SQL proficiency
Strong ML fundamentals: model selection, evaluation, feature engineering
Hands-on experience with PyTorch, scikit-learn, XGBoost
Located within the continental United States

🎁 Benefits

Diverse, collaborative team environment
Generous, flexible paid time off
Stock in an early-stage startup
401(k) with Financial Guidance from Morgan Stanley

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