Staff ML Risk Analyst

Coinbase

Staff ML Risk Analyst at Coinbase defining ML strategies for fraud detection and collaborating with cross-functional teams. Focus on applying ML insights to prevent fraud activities.

Posted 6/16/2026full-timeRemote • 🇺🇸 United StatesLead💰 $193,970 - $228,200 per yearWebsite

Tech Stack

Tools & technologies

PythonSpark

About the role

Key responsibilities & impact

Define the ML data and feature strategy for fraud detection
Own the end-to-end feature engineering pipeline identifying, building, validating and promoting features that drive measurable improvements
Diagnose gaps between current tooling infrastructure and the solutions needed
Partner with Machine Learning Engineers to translate analytical insights into production-ready ML systems
Set technical direction for the ML Analytics function
Partner cross-functionally with Product Managers and Risk analysts to surface fraud signals
Serve as the team's institutional knowledge resource on ML industry evolution

Requirements

What you’ll need

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 this is the core stack.
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 including modern feature stores

Benefits

Comp & perks

Total compensation may also include equity and bonus eligibility
medical, dental, vision, 401(k)

ATS Keywords

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Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learning analyticsdata sciencefeature engineeringSparkPythonbig data MLfeature quality validationpipeline buildinganalytical insights translationfraud detection

Soft Skills

cross-functional collaborationtechnical direction settinginstitutional knowledge resource