Note: The job is a remote job and is open to candidates in USA. Dale WorkForce Solutions is recruiting for a Senior Data Scientist role supporting an advertising services client. The role focuses on designing and executing incrementality tests, media mix models, and causal analyses to measure marketing impact, while translating findings into actionable recommendations and mentoring junior data scientists.
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
Skills
- 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
Benefits
- Remote work arrangement
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