Staff ML Risk Analytics

What you'll do

• Define ML data and feature strategy for fraud detection to ensure high accuracy in model actions.
• Own the end-to-end feature engineering pipeline to drive measurable improvements in ATO and scam ML performance.
• Diagnose gaps between current tooling infrastructure and needed solutions, driving the roadmap to close them effectively.
• Partner with Machine Learning Engineers to translate analytical insights into production-ready systems for continuous improvement.
• Set technical direction for ML Analytics function within Growth & Risk, mentoring junior team members on approach and execution.

What we're looking for

• 8+ years of hands-on experience in machine learning analytics or data science with a focus on risk, fraud, or payments.
• Deep expertise in Spark, Python, and big data ML for feature engineering and model validation at scale.
• Proven ability to diagnose and close gaps between current tooling infrastructure and evolving industry standards.
• Comprehensive understanding of the evolution of the machine learning industry over the past decade.
• Curated approach to solving high-stakes fraud problems with a focus on sensitivity and accuracy in small traffic fractions.
• Background in risk or payments ML, demonstrating intuitive problem framing and domain expertise.