The Role
The Data Scientist will manage end-to-end data projects focusing on knowledge graphs and identity, mentor juniors, and ensure high-quality code and documentation.
Summary Generated by Built In
Samba is a media intelligence company. We know what the world is watching, reading, and thinking about — in real time, at scale, across every screen. Our data exists with the consent of over a billion people, organized into the most complete picture of consumer attention ever built. The biggest brands in the world use that picture to make smarter decisions. We think it’s the most interesting data asset on the planet, because it’s the most culturally relevant.
As a mid-level Data Scientist on Samba's Knowledge Graph & Identity team in Warsaw, you will own end-to-end delivery of significant data science projects with minimal guidance. You are a reliable, autonomous contributor with deep expertise in at least one of Samba's core domains - knowledge graphs, identity spine, measurement, or audience modeling - and the technical range to build production-ready solutions using modern ML and AI methodologies. You'll work closely with peers, product, and engineering, and play an active role in mentoring junior data scientists on the team.
What You'll Do:
- Own end-to-end delivery of significant data science projects — from problem scoping and approach design through to production deployment, with a focus on knowledge graph and identity solutions
- Make sound, independently-reasoned decisions on methodology, model selection, and evaluation; document them clearly in technical solution documents covering problem statement, approach, metrics, and timeline
- Lead solution design for your own initiatives; break down complex epics into well-scoped user stories with clear acceptance criteria, adopting DataOps and MLOps best practices throughout — experiment tracking, pipeline orchestration, model monitoring, and reproducibility
- Build production-quality Python and PySpark code on Databricks — well-tested, documented, and reusable — and implement advanced ML and AI-powered workflows including entity resolution, probabilistic record linkage, embedding-based matching, semantic similarity, and LLM-augmented pipelines
- Develop and maintain reusable tools, libraries, and documentation that improve team efficiency and technical standards; conduct code reviews with constructive, specific feedback that raises the bar
- Mentor junior data scientists on technical execution, code quality, and career development; lead internal talks or workshops on knowledge graphs, identity, or ML topics
- Collaborate cross-functionally with product, engineering, and operations — translate business requirements into technical specifications, partner with data engineering on scalable pipeline design, and participate in cross-functional 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
- 3–5 years of hands-on data science experience with demonstrated ability to own and deliver complex, multi-sprint projects independently
- Advanced 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
- Demonstrated ability to mentor junior data scientists and contribute to team standards
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
Samba is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We strive to empower connection with one another, reflect the communities we serve, and tackle meaningful projects that make a real impact.
Samba may collect personal information directly from you, as a job applicant, Samba may also receive personal information from third parties, for example, in connection with a background, employment or reference check, in accordance with the applicable law. For further details, please see Samba's Applicant Privacy Policy. For residents of the EU , Samba Inc. is the data controller.
Skills Required
- Bachelor's degree in Statistics, Data Science, Computer Science, Mathematics or a related field
- 3-5 years of hands-on data science experience
- Advanced Python, SQL, and PySpark skills
- Experience with Databricks, Delta Lake, and cloud platforms
- Solid command of core ML techniques
- Familiarity with MLOps practices
- Strong communication skills
- Ability to mentor junior data scientists
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The Company
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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