Data Scientist

Write and own production-quality Python code end-to-end — well-structured, tested, documented, and built to last; PySpark proficiency is essential for working with Samba's billion-row viewership datasets

Design, build, and deploy measurement models and statistical frameworks that power Samba’s campaign measurement, reach/frequency estimation, and cross-platform attribution products

Apply the right statistical and ML technique to the right problem — drawing from hierarchical models, Bayesian inference, gradient boosting, regularized regression, causal ML, and probabilistic record linkage — and clearly articulate the reasoning behind your choices

Build and evaluate multi-touch and multi-channel attribution models; apply Causal ML methods — counterfactual modeling, meta-learners (S-learner, T-learner, X-learner), and heterogeneous treatment effect estimation — to advertising and viewership measurement problems

Partner with Data Engineering to define data requirements, validate pipelines, and ensure model inputs are reliable, scalable, and production-ready

Lead technical design reviews and contribute meaningfully to architecture decisions across the Data Science team

Mentor junior Data Scientists through code review, pairing, and structured technical feedback — raising the team's technical floor

Communicate measurement methodologies and findings clearly to technical and non-technical audiences, including senior leadership and external clients

5-7 years of professional data science experience — hands-on, delivery-focused, and measurable in shipped models and production systems

Expert-level Python — clean, modular, testable, production-ready code is your standard, not your aspiration

Advanced PySpark and Databricks — comfortable building and optimizing data pipelines and ML workflows on billion-row datasets

Deep, first-principles command of statistics and ML — you can explain from the ground up how these models work and you apply this understanding to make better modeling decisions

Solid grasp of experimental design — A/B testing, randomization, power analysis, and the conditions under which observational causal inference is appropriate

Fluent in the full ML lifecycle: feature engineering, model evaluation, deployment pipelines, drift monitoring, and iterative improvement in production

Hands-on experience with uplift modeling, synthetic control, difference-in-differences, or propensity-based approaches applied to advertising or media outcomes

Strong ownership mindset — you drive projects independently and are comfortable owning your models from data exploration through production delivery, with minimal hand-holding.

Clear communicator — able to translate statistical reasoning and model behavior into language that drives decisions with product, engineering, and leadership