Senior Data Scientist

The Role

We are looking for a Senior Data Scientist to join our Data Strategy & Analytics team and help clients understand the incremental impact of their marketing investments. In this role, you will design and execute rigorous measurement solutions, including incrementality tests, media mix models, and causal analysis. and translate the results into clear, actionable recommendations.

You will partner with Data Strategy, media, client service, and analytics teams to solve complex measurement challenges across channels and funnel stages. You will work directly with large, imperfect marketing datasets, select the right methodology for each business question, and communicate both the findings and their limitations to technical and non-technical audiences.

The ideal candidate has hands-on experience with incrementality testing and media mix modeling, along with a strong understanding of digital and upper-funnel media measurement. You should be comfortable evaluating channels such as CTV, video, audio, OOH, paid social, and other brand investments where last-touch attribution is incomplete. You will also help strengthen our measurement approaches and guide junior & on-level data scientists in sound experimental and modeling practices.

What We Offer

• 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
• Annual Class Pass credits and more!

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

Nice to Have

• 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