What You'll Do:
- Contribute to data science projects across the full lifecycle — from problem scoping and exploratory analysis through to model development and deployment — with support from senior team members
- Develop and test Python and PySpark code on Databricks, building clean, documented, and reusable solutions for knowledge graph and identity challenges
- Apply core ML techniques — regression, classification, clustering, and model evaluation ,to complex, high-volume datasets, and begin exploring advanced methods such as entity resolution, embedding-based matching, and semantic similarity
- Support MLOps practices including experiment tracking, pipeline orchestration, and model monitoring, adopting DataOps and reproducibility standards throughout
- Break down project requirements into well-scoped tasks with clear acceptance criteria, and contribute to technical documentation covering problem statements, approaches, and metrics
- Collaborate cross-functionally with product, engineering, and operations — translating business requirements into technical tasks and participating in design reviews and working groups
Who You Are:
- Bachelor's degree required in Statistics, Data Science, Computer Science, Mathematics or a related quantitative field; Master's strongly preferred
- 1 to 2 years of hands-on data science experience with demonstrated ability to own and deliver complex, multi-sprint projects independently
- Python with production-quality code, testing, and documentation; strong SQL and PySpark for billion-row datasets
- Databricks workflows, Delta Lake, and job orchestration; working knowledge of cloud platforms (AWS or GCP)
- Solid command of core ML — regression, classification, clustering, model evaluation, and experimental design — applied to complex, high-volume data
- Proficiency with MLOps practices: experiment tracking, pipeline orchestration (Airflow), and reproducible model deployment
- Exposure to modern AI methodologies: RAG systems, LLM-augmented models, vector databases, and semantic search
- Strong communicator — able to translate technical work into clear documentation, user stories, and cross-functional conversations
Preferred skills:
- Hands-on experience with knowledge graph construction, entity resolution, or semantic data modeling (RDF, OWL, SPARQL, or equivalent graph frameworks)
- Familiarity with probabilistic record linkage, identity graph approaches, or embedding-based entity matching at scale
- Experience with causal inference methods (A/B testing, synthetic control, uplift modeling)
- Experience with deduplication, enrichment, or web-to-TV linkage problems
- Background in media, ad tech, or measurement — TV viewership (ACR/STB data), digital audience modeling, cross-platform measurement (linear + CTV/OTT), or identity resolution in privacy-constrained environments
- Familiarity with the measurement and identity vendor landscape (Nielsen, Comscore, LiveRamp, The Trade Desk
Skills Required
- Bachelor's degree in Statistics, Data Science, Computer Science, Mathematics or related field; Master's preferred
- 1 to 2 years hands-on data science experience
- Proficiency in Python with production-quality coding, testing, and documentation
- Strong SQL and PySpark skills for large datasets
- Experience with Databricks workflows and cloud platforms (AWS or GCP)
- Knowledge of core ML techniques and experimental design
- Familiarity with MLOps practices and reproducible model deployment
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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