The opportunity
Ready to do the most impactful work of your career? At Coinbase , we are uncompromising on our mission to increase economic freedom.
What you'll do
Own the design, build, and maintenance of production data pipelines,: dimensional models, and ML-powered analytics products (including LLM-based contact classification, friction detection, and issue attribution) serving CX and compliance use cases.
Drive the development of self-service dashboards and AI-assisted analytics: tools in Looker, Hex, or Python visualization libraries that reduce time-to-insight for CX stakeholders and operational teams.
Design and execute causal inference frameworks and quasi-experiments (A/B: tests, holdout frameworks, difference-in-differences analyses) to measure the incremental impact of CX programs on customer retention and product engagement.
Partner with cross-functional stakeholders to translate business needs into: scalable data solutions, managing a long-term analytics roadmap that balances tactical delivery with strategic investment.
Lead deep-dive investigations into key performance metrics using advanced: statistical methods (Bayesian reasoning, time series analysis, propensity score matching) to identify actionable opportunities that improve customer outcomes, operational efficiency, and compliance posture.
Shape analytics engineering standards, documentation practices, and testing: frameworks that enable the broader team to move faster with higher data quality and statistical rigor.
What they're looking for
- + years of experience in analytics engineering, data science, or data: engineering, with hands-on ownership of production data pipelines, dimensional data models (star/snowflake schemas), and statistical or ML models in dbt, Airflow, Snowflake, or similar.
- Advanced SQL and Python proficiency applied to data model development,: pipeline orchestration, causal inference, statistical analysis, and ML model deployment - not limited to scripting or visualization.
- Demonstrated experience designing and executing A/B tests, quasi-experiments,: and causal inference methods (difference-in-differences, propensity score matching) with the statistical rigor required for executive reporting and regulatory defensibility.
- Proven success building production-grade dashboards and self-serve analytics: solutions in BI tools (Looker, Tableau, Hex, or similar) that measurably reduced stakeholder dependency on ad-hoc requests.
