About this role

▸about Coinbase — company background

Ready to do the most impactful work of your career? At Coinbase, we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase.

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. Fraud at Coinbase is fast-evolving, with professional, adaptive counterparties that outpace any human response team. You'll build and shape the ML-powered, automated solutions that detect and prevent account takeover (ATO) and scam activity before it reaches our users, setting the technical direction for the ML Analytics function within Growth & Risk.

What you'll do:

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.

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