- 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
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