Data Scientist, Samba TV

• 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
• 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

Desirable

• 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'll work closely with peers, product, and engineering, and play an active role in mentoring junior data scientists on the team
• 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