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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:
Hands-on, proactive, and analytical professionals who are passionate about using data to solve complex, real-world problems 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.
Languages
- English
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