ABOUT THE ROLE
We are looking for a hands-on Data Scientist to own and deliver complex measurement science and modeling work at the core of our measurement and audience sciences products.
The role requires a deep, first-principles understanding of data science and machine learning — not just the ability to apply libraries, but the ability to reason clearly about model behavior, articulate trade-offs between approaches, and make defensible methodological decisions under ambiguity. This is emphatically a coding role — you will spend the majority of your time writing production-quality Python, building and evaluating models on large-scale viewership and web data, and delivering end-to-end ML solutions.
You will work closely with Data Engineering, Product, and go-to-market teams.
WHAT YOU'LL DO
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
WHO ARE YOU
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
Experience with multi-touch attribution (MTA) or multi-channel attribution modeling — understanding of the limitations of rule-based approaches and the methodological trade-offs of data-driven alternatives
Hands-on experience with Causal ML methods — counterfactual modeling, meta-learners, and heterogeneous treatment effect estimation — applied to advertising or media measurement outcomes
Direct exposure to TV or digital viewership data — ACR signals, STB data, viewership panels, or cross-platform measurement (linear + CTV/OTT)
Familiarity with the measurement
t vendor landscape (Nielsen, Comscore, VideoAmp, iSpot) and industry standards (MRC, GRP/TRP frameworks)
Advanced degree (MS or PhD) in Statistics, Mathematics, Computer Science, or a related quantitative field — or equivalent depth demonstrated through work
Skills Required
- 5-7 years of professional data science experience
- Expert-level Python
- Advanced PySpark and Databricks
- Deep understanding of statistics and machine learning
- Hands-on experience with uplift modeling and causal ML methods
- Advanced degree in a quantitative field
What We Do
Television remains a vibrant cultural influence and an essential source of entertainment and information worldwide. Tremendous growth in content choices, and viewing platforms that allow us to watch anything, anytime, on any screen, has actually made it harder for viewers to discover and keep up with all the great programming available. It’s also more competitive for content providers to keep your attention, and for marketers to make strong, measurable connections with their target consumers. Technology that improves the viewing experience, enables content discovery, and addresses audience fragmentation across screens will strengthen television’s business model and relevance to consumers. Data is at the center of any solution to make TV better. Samba TV's technology is built into Smart TVs and easily maps to smart phones and tablets. By recognizing what's on screen, Samba TV learns what viewers like and using machine learning algorithms, enables discovery of shows and actors in a whole new way. Likewise, our data and measurement products are transforming the way stakeholders across the media landscape are thinking about their business. Given the dramatic growth in streaming services, connected devices, time-shifting, and multi-screen viewership, our data products solve real problems and create a meaningful competitive advantage for our clients.
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