Job Description:

• Own the model lifecycle: requirements, experimentation, model development, evaluation, and model cards, partnering with ML engineers on deployment and production infrastructure
• Translate complex fraud patterns into well-framed ML solutions: defining what to model, what success looks like, and where ML adds value vs. simpler approaches
• Design and maintain feature engineering pipelines for model development
• Monitor model quality in production, tracking performance over time, detecting data drift, and determining when to retrain
• Partner closely with leadership, go-to-market, fraud operations, product, and engineering teams to define and execute effective fraud strategies
• Champion a culture of continuous learning, experimentation, and collaboration across the fraud and broader data science teams

Requirements:

• Bachelor's degree or higher in a quantitative field such as Mathematics, Statistics, Computer Science, Engineering, Operations Research, Physics or related field
• 3+ years of work experience building and deploying machine learning systems into production
• Strong proficiency in Python and SQL
• Strong understanding of ML fundamentals: model selection, evaluation methodology, feature engineering, and common failure modes
• Hands-on experience with PyTorch, scikit-learn, and XGBoost (or similar gradient boosting frameworks)
• High attention to detail, strong intellectual curiosity, and a deep understanding of user behavior and fraud patterns
• Empathetic, humble, and collaborative team player
• Candidates must be located within the continental United States.

Benefits:

• Competitive salary based on experience, with full medical and dental & vision benefits.
• Stock in an early-stage startup growing quickly.
• Generous, flexible paid time off policy.
• 401(k) with Financial Guidance from Morgan Stanley.