Staff ML Risk Analytics

Coinbase · Remote - USA

Experience: Staff

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 Machine Learning Analytics professional on the Growth & Risk team, you will 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 a fast-evolving problem: our counterparties are professional, adaptive, and operate faster than any human response team can. That's why we build ML-powered, automated solutions. Your work will directly determine how well our systems can detect and prevent account takeover (ATO) and scam activity before it reaches our users. This is not a traditional risk analyst role. We are not looking for rule-writers. We are looking for someone who understands how the ML industry has evolved and can apply that knowledge to hard, high-stakes fraud problems. What you’ll be doing: Define the ML data and feature strategy for fraud detection, determining what data needs to enter our systems so our 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 your understanding of how the indu

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