Data Scientist - (Knowledge Graph & Identity)

Warsaw,WarsawInternational Full Time Employeeentry

Data ScienceData ScientistDataData & AI

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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 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.

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

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

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