Data Science Manager (Knowledge Graph & Identity)

WarsawInternational Full Time Employeeentry

Data ScienceOtherData Science ManagerData & AI

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Samba is an AI-powered media intelligence company on a mission to give marketers the complete picture of their audiences. Our AI indexes media consumption across millions of smart TVs and 2.

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Data ScienceOtherData Science ManagerData & AI

Samba is an AI-powered media intelligence company on a mission to give marketers the complete picture of their audiences. Our AI indexes media consumption across millions of smart TVs and 2.5 billion web pages, combining that data with third-party signals through the Samba Knowledge Graph, a map of the real interests, behaviors, and purchase intent of 1.5 billion user profiles globally. Brands, agencies, publishers, and platforms use Samba to make smarter decisions across every stage of the marketing funnel.

As the Data Science Manager in our Warsaw office, you will lead and grow a team of 4–6 data scientists building Samba's knowledge graph and identity solutions - foundational capabilities that power our measurement and audience intelligence products. You’ll translate business challenges into technical roadmaps, oversee end-to-end delivery and cultivate a high-performance culture grounded in experimentation, collaboration and continuous learning. Your primary focus is your people: hiring exceptional talent, setting clear goals, and creating an environment where they do their best work. You bring a solid data science background that lets you engage meaningfully in technical scoping, solution design, and code reviews, and jump in hands-on when the team needs it.

This role reports to the VP of Data Science.

Lead, mentor, and develop a team of 4–6 data scientists, owning performance reviews, career development, goal-setting, and promotion readiness aligned to Samba's leveling framework

Define meaningful KPIs for each team member; hold regular 1:1s to track progress, surface blockers, and support growth

Drive hiring, onboarding, and a high-engagement team culture grounded in psychological safety, continuous learning, and accountability

Partner with product, engineering, and analytics to align priorities and translate the team's roadmap into well-scoped quarterly plans and sprint commitments

Own delivery execution — sprint planning, backlog grooming, dependency management, and blocker removal — and communicate progress, risks, and blockers clearly to senior leadership

Participate actively in technical scoping and solution design across data science initiatives, with a focus on knowledge graph, entity resolution, and identity-linked audience modeling

Champion best practices across teams — code reviews, modular architectures, documentation — foster knowledge-sharing, and jump in hands-on on high-complexity work as needed

Bachelor's degree required in Statistics, Data Science, Computer Science, Mathematics or related field; Master's preferred

5-7 years of hands-on data science experience with 1-2+ years in a direct people-management or team-lead role with demonstrated ability to develop, retain, and hire data scientists

Solid command of core statistics and ML - hypothesis testing, probability, regression, classification, clustering, model evaluation, and experimental design

Strong Python (pandas, NumPy, PySpark, scikit-learn) and SQL; Databricks or similar platform experience essential

Familiarity with MLOps practices: experiment tracking, pipeline orchestration (Airflow), reproducible model deployment

Detail-oriented and proactive in anticipating delivery risks

Comfortable running Agile ceremonies and maintaining consistent sprint cadence across a distributed team

Strong communicator - able to give direct, constructive feedback and advocate for your team to key stakeholders

Hands-on experience with knowledge graph construction, ontology design, or semantic data modeling (RDF, OWL, SPARQL, or equivalent graph frameworks)

Familiarity with entity resolution, probabilistic record linkage, or identity graph approaches at scale

Experience applying graph-based or embedding-based methods to real-world entity matching, deduplication, or enrichment problems

Exposure to modern AI methodologies: RAG pipelines, embedding models, vector search, and LLM-based approaches

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) a plus

Experience with causal inference (A/B testing, synthetic control, uplift modeling) a plus