Staff ML Risk Analytics
8.0/10
Coinbase
$98,000 – $162,000 USD13.3% below market
Remote
senior
about 3 hours ago
aianalyticscryptofintechmachine learningdata scienceSparkPythonbig dataSQL
AI SummaryVerified by Aipplify AI
The vacancy is well-structured, providing clear expectations and requirements, though some details on compensation could be improved.
AI quality score8.5 / 10
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Overview
Join Coinbase as a Staff ML Risk Analytics professional to tackle sophisticated fraud using machine learning. Work remotely and collaborate with a dynamic team focused on enhancing fraud detection and prevention. Coinbase is a remote-first company committed to increasing economic freedom. We build the future of finance with a focus on innovation and high standards.
What you’ll be doing
- •Define the ML data and feature strategy for fraud detection.
- •Own the end-to-end feature engineering pipeline.
- •Diagnose gaps between current tooling infrastructure and needed solutions.
- •Partner with Machine Learning Engineers to translate insights into production-ready ML systems.
- •Set technical direction for the ML Analytics function and mentor junior team members.
- •Partner cross-functionally with Product Managers and Risk analysts.
- •Serve as the team's institutional knowledge resource on ML industry evolution.
What we offer
- •Base salary range: $98K - $162K.
- •Total compensation may include equity and bonus eligibility, and benefits (medical, dental, vision, 401(k)).
- •Equal Opportunity Employer commitment.
- •Reasonable accommodations for individuals with disabilities.
What we look for in you
- •8+ years of hands-on experience in machine learning analytics or related fields.
- •Deep expertise in Spark, Python, and big data ML.
- •Proven experience in feature engineering for ML models.
- •Holistic understanding of ML industry evolution.
- •Background in risk or payments ML is strongly preferred.
- •A passion for fighting fraud and abuse.
- •Ability to responsibly use generative AI tools in workflows.
Skills
machine learningdata scienceSparkPythonbig dataSQL
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