Responsibilities

• 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

Requirements

• 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

Preferred

• 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

Benefits

• Remote-first culture
• Unlimited PTO
• Extended Holiday break (Winter)
• Flexible schedules
• Work from anywhere options*
• 100% paid parental leave
• 401(k) matching
• Medical, Dental, Vision, Life, Pet Insurance
• Sponsored life insurance
• Short Term Disability insurance and additional voluntary insurance