Data Science Director (Measurement)

Join Samba, a leading company in measurement science, as the Director of Data Science. In this role, you will lead the Measurement science team, responsible for building statistical frameworks, causal models, and attribution methodologies. You will work closely with Product and Engineering to shape direction and bring solutions to production. You will also own the science and delivery for a portfolio spanning incrementality measurement, multi-touch attribution, reach and frequency modeling, and audience intelligence.

• Lead the Measurement science team responsible for building statistical frameworks, causal models, and attribution methodologies.
• Own the science and delivery for a portfolio spanning incrementality measurement, multi-touch attribution, reach and frequency modeling, and audience intelligence.
• Drive the application of Causal ML and other measurement science methodologies, ensuring high-quality work across multiple concurrent workstreams.
• Bachelor's degree required in Statistics, Computer Science, Mathematics, or a related quantitative field; Master's or PhD strongly preferred
• Track record of owning and delivering complex, multi-workstream data science projects on time in a fast-moving environment
• Excellent communicator - able to translate causal and statistical reasoning into language that drives product, sales, and executive decisions
• Hands-on experience with multi-touch attribution (MTA) or multi-channel attribution modeling - understanding of rule-based limitations and the methodological trade-offs of data-driven alternatives
• Expert-level Python and SQL; strong PySpark and Databricks experience for large-scale measurement pipelines
• 8+ years of hands-on data science experience with at least 2-3 years in a people management role, including demonstrated ability to hire, develop, and retain senior data scientists
• Familiarity with the measurement vendor landscape (Nielsen, Comscore, VideoAmp, iSpot) and industry standards (MRC accreditation, GRP/TRP frameworks)
• Experience with audience segmentation, identity resolution, or privacy-preserving measurement approaches
• Direct experience with TV or digital measurement - ACR/STB data, viewership panels, reach/frequency modeling, or cross-platform measurement (linear + CTV/OTT)
• Deep, first-principles expertise in Causal ML - counterfactual modeling, meta-learners, and heterogeneous treatment effect estimation - applied to advertising measurement or media outcomes; familiarity with EconML, DoWhy, or CausalML a plus
• Strong statistical and ML foundations - regression, classification, experimental design, model evaluation, and the ability to reason clearly about trade-offs between modeling approaches
• Solid command of the broader measurement science toolkit: A/B testing, difference-in-differences, synthetic control, propensity scoring, Bayesian hierarchical models, and panel methodology - with clear intuition for when to apply each approach and what its limitations are
• Track record of publishing research, white papers, or presenting at industry conferences