Staff ML Risk Analyst

Details

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. Coinbase is a remote-first (but not remote-only) company; expect to get together quarterly for in-person working sessions called “surges.”

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. You'll build and shape ML-powered, automated solutions that detect and prevent account takeover (ATO) and scam activity before it reaches users, and set the technical direction for the ML Analytics function within Growth & Risk.

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 the small fraction of traffic where intervention matters most.
• Own the end-to-end feature engineering pipeline: identify, build, validate, and promote features that drive measurable improvements in ATO and scam ML performance.
• Diagnose gaps between current tooling/infrastructure and the solutions needed; drive the roadmap to close them by applying deep knowledge of ML architectural evolution.
• 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 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.

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.
• Practitioner-level proficiency in Spark, Python, and big data ML as a core working stack; 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 such as Tecton and 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.

Compensation & Benefits

• Annual base salary range (excluding equity and bonus): $193,970 — $228,200 USD (base varies by location).
• Total compensation may also include equity, bonus eligibility, and benefits (medical, dental, vision, 401(k)).

Location & Work Model

• Remote within the United States (remote-first company with quarterly in-person "surges").

Additional Information

• Application limit: Candidates may submit a maximum of 3 applications within a 6-month period.
• Equal Opportunity Employer statement, accommodations contact, US applicant notices (Employee Rights, Know Your Rights, E-Verify), and an AI disclosure about piloted screening tools are included in the posting.