Staff ML Risk Analyst

Responsibilities

• Define the ML data and feature strategy for fraud detection, determining what data needs to enter systems so models can take intelligent, high-accuracy action on a 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, and drive the roadmap to close them leveraging understanding of how the ML industry has evolved.
• Partner with Machine Learning Engineers to translate analytical insights into production-ready ML systems, ensuring models are instrumented, monitored, and continuously improved.
• Set technical direction for the ML Analytics function within Growth & Risk, mentoring junior team members.
• Partner cross-functionally with Product Managers and Risk analysts to surface fraud signals and translate ML findings into business-impacting decisions.
• Serve as the team's institutional knowledge resource on ML industry evolution.

Requirements

• 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.
• 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 — from Hadoop-era big data to modern feature stores like Tecton.
• A curated, high-precision approach to ML problems: optimizing for sensitivity and accuracy on a small fraction of high-stakes traffic.
• Demonstrates the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows.

Nice to Haves

• Experience with modern ML feature stores (Tecton, Feast, or equivalent).
• Prior work at FinTech companies, payments platforms, or risk solution vendors.
• Familiarity with crypto-specific fraud vectors including ATO, scam flows, and onchain transaction patterns.