Principal Data Scientist

Ibotta is seeking a Principal Data Scientist, Marketing Analytics to serve as a strategic technical leader and the highest level of individual contributor within our data science organization. You'll drive business-critical initiatives while establishing technical standards that elevate our entire organization's capabilities, operating at a seniority level comparable to Director, and carrying additional emphasis on deep technical expertise and problem-solving.

A core focus will be enhancing and partnering to evolve Ibotta's productized measurement solutions that demonstrate how we drive incremental sales for brand partners at scale. You'll collaborate across teams to understand edge cases, design and validate innovation and experiments, and ensure our measurement products maintain the rigor required of industry leading organizations. ​

This position is available in multiple locations:

Denver, Colorado - if you are in or willing to relocate to the Denver area, this is a hybrid position requiring 3 days in office (Tuesday, Wednesday, and Thursday).
 

Remote options are available for the following states - AZ, AR, CA, FL, GA, IL, IN, IA, KS, MD, MA, MI, MN, MO, MT, NV, NJ, NY, NC, OH, OK, PA, TN, TX, UT, VA, WA, WI. If you’re not in the Denver metro area or the listed states, relocation reimbursement to Denver is available.

What You Will Be Doing:

Measurement Science & Productized Solutions:

• 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

Technical Excellence & Code Quality:

• 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

Strategic Leadership & Collaboration:

• 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. 

Mentorship & Organizational Impact:

• 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

Embrace and uphold Ibotta's Core Values: Integrity, Boldness, Ownership, Teamwork, Transparency, & A good idea can come from anywhere

What We Are Looking For:

• 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 co