Define the ML data and feature strategy for fraud detection
Own the end-to-end feature engineering pipeline identifying, building, validating and promoting features that drive measurable improvements
Diagnose gaps between current tooling infrastructure and the solutions needed
Partner with Machine Learning Engineers to translate analytical insights into production-ready ML systems
Set technical direction for the ML Analytics function
Partner cross-functionally with Product Managers and Risk analysts to surface fraud signals
Serve as the team's institutional knowledge resource on ML industry evolution

8+ years of hands-on experience in machine learning analytics, data science, or a related technical field with meaningful experience applied to risk, fraud, or payments problems.
Deep, practitioner-level expertise in Spark, Python, and big data ML this is the core stack.
Proven experience in feature engineering for ML models, including identifying the right signals, building pipelines, and validating feature quality at scale.
Holistic understanding of how the ML industry has evolved over the past decade including modern feature stores

Staff ML Risk Analyst

Key skills