Data Scientist

Warsaw,WarsawInternational Full Time Employeeentry

Data ScienceData ScientistDataData & AI

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Overview

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 ScienceData ScientistDataData & 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 a mid-level Data Scientist at Samba 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 — measurement, or audience modelling — 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.

Own end-to-end delivery of significant data science projects — from problem scoping and approach design through to production deployment

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 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

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

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