Partnering with Data Strategy, media, and client teams to translate business questions into clear, testable measurement plans
Designing and analyzing incrementality tests, including geo-based experiments, holdouts, matched-market tests, and other causal inference approaches
Building, validating, and interpreting media mix models to estimate channel contribution, efficiency, saturation, and diminishing returns
Developing measurement approaches for upper-funnel and brand media, including its direct impact and influence on lower-funnel outcomes
Conducting power analyses, test feasibility assessments, sensitivity analyses, and model diagnostics to ensure findings are statistically credible
Working with large, multi-source marketing datasets; identifying data quality issues, measurement gaps, and implications for analysis
Turning analytical findings into practical recommendations for media planning, optimization, and future testing
Applying complementary advanced analytics methods - including predictive modeling, propensity modeling, segmentation, and forecasting - to solve broader client and media strategy questions
Guiding and mentoring junior data scientists and contributing to shared measurement standards, code, and best practices
Education: Master’s degree in Statistics, Economics, Data Science, Computer Science, Engineering, or another quantitative discipline preferred or B.S. + 5 years of relevant experience
Strong programming skills in Python, R, & SQL
Hands-on experience designing and analyzing incrementality tests, such as geo holdouts, matched-market tests, synthetic controls or holdouts, or randomized experiments
Hands-on experience building, validating, and interpreting media mix models
A deep understanding of statistical modeling, causal inference, experimental design, and time-series methods
Ability to evaluate methodological tradeoffs, challenge weak assumptions, and select approaches appropriate to the available data and business decision
Ability to work independently on ambiguous problems while collaborating closely with cross-functional teams
Experience with Bayesian modeling frameworks such as PyMC or similar tools
Experience calibrating or validating MMM results with incrementality tests, or integrating multiple measurement methods into a unified recommendation
Experience with brand measurement, awareness studies, retail or offline sales data, or multi-outcome/funnel modeling