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Salary: USD 180,370 - 212,200 / annual
Coinbase is seeking a Senior Data Scientist to join the Product team in a remote-first role. You will be an integral part of the data science function, working in partnership with Product, Engineering, and Design to influence the product roadmap and drive business impact through deep expertise in experimentation, analytics, and advanced modeling.
In this role, you will conduct analysis and deep dives on ambiguous business problems, producing insights and recommendations that guide team decision-making. You will build, tune, and measure the performance of online and offline ML models and AI systems used across Coinbase. You will own a broad scope of data and metrics, from definitions and core logging to presentation of data visualizations. You will guide code reviews, provide SQL and Python expertise, and create well-maintained ETL jobs. You will maintain a high bar for statistical rigor on your team, designing, executing, and upleveling experimentation and causal analyses that build stakeholder confidence.
Coinbase is a remote-first but not remote-only company. Expect to gather quarterly for intense in-person working sessions called "surges." The environment is intense and high-performing; this is a place to be pushed past your perceived limits and work alongside people who refuse to settle for "good enough."
REQUIREMENTS:
- BA/BS in a quantitative field (Math, Stats, Physics, Computer Science) with ≥5+ years of relevant experience, OR PhD in a quantitative field with ≥3+ years of relevant experience
- Demonstrated experience driving impactful data science projects in ambiguous problem spaces
- Ability to influence external stakeholders by synthesizing data learnings into compelling stories
- Practical expertise applying complex modeling frameworks to business problems
- Professional experience with SQL and Python
- Experience working with digital products in an iterative development cycle
- Demonstration of core cultural values: clear communication, positive energy, continuous learning, efficient execution
- Responsible use of generative AI with human oversight to deliver business-ready analytical outputs and models
