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Staff ML Risk Analyst

Coinbase

Role at a glance

Category
Risk
Work arrangement
Remote
Location
Remote
Salary range
$193,970
Posted
Jun 17, 2026
Apply for this roleApplications go directly to Coinbase
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Coinbase is hiring a Staff ML Risk Analyst in Remote. This is a Risk role in the governance, risk, and compliance field, with a posted range of $193,970. Review the full details below and apply directly with Coinbase.

Ready to do the most impactful work of your career? At a and utm_source=coinbase 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.” a learn more about working at Coinbase. As a Staff ML Risk Analyst on the Growth and Risk team within the a Consumer and 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 and 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. 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

Full responsibilities and requirements are on Coinbase's application page.

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About Coinbase
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Location and market context

This is a remote risk management role, so it draws from a national talent pool rather than a single metro. Remote governance and compliance roles reward candidates who can show they work effectively across time zones and distributed legal, security, and product teams. Confirm any residency, travel, or occasional-onsite expectations directly with Coinbase.

About risk management roles

Risk roles own the methodology for identifying, assessing, and escalating enterprise, operational, and technology risk. Second-line teams set risk appetite and challenge the first line. Roles like this one are typically evaluated against frameworks such as enterprise and operational risk frameworks, NIST AI RMF, and risk-appetite and escalation practices.

How to position yourself for this risk management role

Strong candidates emphasize risk assessment methodology, appetite and escalation, cross-functional partnership, and clear reporting to senior leadership and the board. In your resume and outreach, tie your experience to how Coinbase would apply enterprise and operational risk frameworks, NIST AI RMF, and risk-appetite and escalation practices, and lead with concrete outcomes rather than duties.

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