Staff ML Risk Analyst

About the role

As a Staff ML Risk Analyst on the Growth & Risk team within the Consumer & Business group, you'll sit at the intersection of fraud intelligence and machine learning infrastructure, defining how we identify, model, and respond to sophisticated fraud at scale.

Responsibilities

• Define the ML data and feature strategy for fraud detection, determining what data needs to enter our systems so models can take intelligent, high-accuracy action on the small fraction of traffic where intervention matters most.
• Own the end-to-end feature engineering pipeline, identifying, building, validating, and promoting features that drive measurable improvements in ATO and scam ML performance.
• Diagnose gaps between current tooling infrastructure and the solutions needed, driving the roadmap to close them by applying deep knowledge of how the ML industry has evolved architecturally.
• Partner with Machine Learning Engineers to translate analytical insights into production-ready ML systems, ensuring models are instrumented, monitored, and continuously improved.
• Mentor junior team members across the ML Analytics function, defining the technical approach and translating direction into execution.
• Partner cross-functionally with Product Managers and Risk Analysts to surface fraud signals and translate ML findings into business-impacting decisions.

Required Skills and Experience

• 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.
• Practitioner-level proficiency in Spark, Python, and big data ML as a core working stack, with demonstrated ability to operate beyond SQL and rule-based approaches.
• Proven experience in feature engineering for ML models, including identifying signals, building pipelines, and validating feature quality at scale.
• Working knowledge of the ML infrastructure landscape evolution, from Hadoop-era big data through modern feature stores (e.g., Tecton, Feast), with the ability to apply that context to close infrastructure gaps.
• Demonstrated ability to optimize ML systems for sensitivity and accuracy on high-stakes, low-volume fraud traffic rather than broad-coverage, high-volume use cases.
• Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.