Principal Data Scientist leading marketing analytics initiatives at Ibotta. Collaborating across teams to enhance measurement solutions and drive business impact through data science.
Responsibilities
Help lead the continued evolution of Ibotta's measurement methodology through exploration of cutting-edge measurement research and experimental design
Collaborate as the go-to troubleshooter for measurement anomalies—exploring outlier results, diagnosing data quality issues, and validating statistical assumptions across alpha, beta, and production measurement systems
Lead code reviews and architecture discussions, providing expert guidance on design patterns, scalability, and technical trade-offs
Foster a culture of code quality, rigorous measurement, and collaborative problem-solving
Evaluate and adopt new tools, explore frameworks and innovative model forms that enhance team effectiveness, while maintaining code quality standards
Lead enterprise-wide data science initiatives spanning 6+ months with measurable business impact across multiple teams and KRs
Build trusted partnerships with C-suite executives, product managers, and data science leaders to translate business problems into technical solutions while delivering with consistency and transparency
Present complex technical concepts clearly to both technical and non-technical audiences, including executive leadership
Deliver high impact presentations on key initiatives and research, with easily understood visualizations, to drive insight and adoption
Mentor more junior data scientists through technical guidance, code reviews, and strategic coaching
Create technical training programs and documentation that elevate organizational data science maturity
Requirements
10+ years of professional experience in data science, machine learning, or advanced analytics with demonstrated transformational impact
Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, Data Science, or related quantitative field required; Master's or Ph.D. strongly preferred
Experience in performance marketing, retail media, e-commerce, or CPG analytics environments strongly preferred
Expert-level SQL and Python with demonstrated ability to write clean, maintainable, well-tested production-grade code
Experience with distributed computing (Spark, PySpark) and cloud platforms (AWS, GCP, Azure) is a requirement
Strong software engineering practices: version control (Git), CI/CD, unit testing, code review, design patterns
Advanced ML frameworks and techniques (time series, ensemble methods) and MLOps practices (model deployment, monitoring, feature engineering) strongly preferred
Deep expertise in experimental methods like RCTs and AB testing at scale, along with quasi-experimental designs: difference-in-differences, propensity score matching, regression discontinuity, and similar modalities
Deep understanding of performance marketing metrics (ROAS, incrementality, new-to-brand acquisition), and ability to quantify and communicate business impact.
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