Staff ML Risk Analyst
Staff-level ML Analytics role focused on fraud detection at Coinbase. Own feature engineering pipelines, define ML data strategy, and partner with MLEs to productionize models that detect account takeover and scam activity.
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 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 understanding of how the ML industry has evolved.
- Partner with Machine Learning Engineers to translate analytical insights into production-ready ML systems, ensuring models are instrumented, monitored, and continuously improved.
- Set technical direction for the ML Analytics function within Growth & Risk, mentoring junior team members.
- Partner cross-functionally with Product Managers and Risk analysts to surface fraud signals and translate ML findings into business-impacting decisions.
- Serve as the team's institutional knowledge resource on ML industry evolution.
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.
- Deep, practitioner-level expertise in Spark, Python, and big data ML.
- Proven experience in feature engineering for ML models, including identifying the right signals, building pipelines, and validating feature quality at scale.
- Holistic understanding of how the ML industry has evolved over the past decade — from Hadoop-era big data to modern feature stores like Tecton.
- A curated, high-precision approach to ML problems: optimizing for sensitivity and accuracy on a small fraction of high-stakes traffic.
- Demonstrates the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows.
Nice to Haves
- Experience with modern ML feature stores (Tecton, Feast, or equivalent).
- Prior work at FinTech companies, payments platforms, or risk solution vendors.
- Familiarity with crypto-specific fraud vectors including ATO, scam flows, and onchain transaction patterns.
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