Note: The job is a remote job and is open to candidates in USA. Coinbase is a company focused on increasing economic freedom and is seeking a Staff ML Risk Analyst to join their Growth & Risk team. The role involves defining fraud detection strategies using machine learning and collaborating with various teams to enhance fraud prevention measures.
Responsibilities
- 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
Skills
- 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, with demonstrated 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 (e.g., Tecton, 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
Benefits
- Equity and bonus eligibility
- Benefits (medical, dental, vision, 401(k))
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