Director Data Science, Measurement

WHAT YOU'LL DO

Measurement Science Strategy: Partner with Product to define the measurement science roadmap - spanning incrementality, multi-touch attribution, reach/frequency estimation, panel calibration, and audience targeting. Translate business and client priorities into well-scoped quarterly plans and sprint commitments.

Technical Leadership: Lead design reviews and architecture decisions across the team. Drive the application of Causal ML - counterfactual modeling, meta-learners, and heterogeneous treatment effect estimation - as the primary framework for measuring ad effectiveness, alongside rigorous command of the broader toolkit: A/B testing, DiD, synthetic control, Bayesian hierarchical models, and panel methodology.

Delivery & Execution: Own end-to-end delivery across the team's measurement science portfolio - managing dependencies, removing blockers, and ensuring timely delivery of high-quality work across multiple concurrent workstreams.

Scientific Standards & Data Quality: In partnership with Engineering, set and maintain standards for experimental design, model evaluation, reproducibility, and production readiness. Co-own the data quality framework that ensures the robustness and consistency of measurement products used by clients and internal stakeholders.

Best Practices & MLOps: In partnership with Engineering, develop and implement best practices for the full DS lifecycle - data management, modeling, evaluation, pipeline orchestration, and production deployment on Databricks/Spark.

People & Team Leadership: Lead, mentor, and grow a team of data scientists - owning hiring, performance reviews, career development, and individual goal-setting aligned to Samba's leveling framework. Build a high-accountability culture grounded in technical rigor, psychological safety, and continuous learning.

Cross-functional Partnership: Collaborate with Product, Engineering, and Business Development to define and execute data-driven measurement projects. Support Sales and Marketing with technical positioning and clear articulation of Samba's measurement capabilities.

Stakeholder Communication: Translate complex causal and statistical findings into clear, actionable insights for technical and non-technical audiences. Represent the team to senior leadership and external stakeholders, and contribute a credible technical voice to client conversations and industry forums.

WHO YOU ARE

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

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

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

Strong statistical and ML foundations - regression, classification, experimental design, model evaluation, and the ability to reason clearly about trade-offs between modeling approaches

Expert-level Python and SQL; strong PySpark and Databricks experience for large-scale measurement pipelines

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

Bachelor's degree required in Statistics, Computer Science, Mathematics, or a related quantitative field; Master's or PhD strongly preferred

Direct experience with TV or digital measurement - ACR/STB data, viewership panels, reach/frequency modeling, or cross-platform measurement (linear + CTV/OTT)

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

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

Track record of publishing research, white papers, or presenting at industry conferences