About The Role

You Will Be

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

You Must Have

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

Nice to Have

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Experience with Bayesian modeling frameworks such as PyMC or similar tools
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Experience calibrating or validating MMM results with incrementality tests, or integrating multiple measurement methods into a unified recommendation
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Experience with brand measurement, awareness studies, retail or offline sales data, or multi-outcome/funnel modeling