Responsibilities
- Lead development of new fraud prediction models using a mix of approaches for tabular, graph, and behavioral data
- Build and scale feature pipelines and training datasets from proprietary and third-party signals, partnering with data and platform teams when needed
- Prototype new modeling ideas and features, run offline experiments, and drive the best-performing approaches into production with appropriate risk controls
- Productionize models: integrate into batch and/or real-time decision systems, and improve reliability, latency, and operational robustness
- Instrument and monitor model and data health, and help define retraining/backtesting workflows as fraud patterns evolve
- Identify and implement foundational improvements to how the team builds models
- Collaborate across Engineering, Fraud Analytics, Product, and ML Platform to define requirements, evaluate tradeoffs, and communicate results clearly to both technical and non-technical audiences
Team
Structure: Work closely with experienced ML engineers, platform partners, and cross-functional stakeholders
