Data Scientist

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 Junior Data Scientist at Samba in Amsterdam, 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 — Identity and 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.

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 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 Be a team player and collaborate with different teams. 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 2+ years of hands-on data science experience with demonstrated ability to own and deliver complex, multi-sprint projects independently Advanced Python ( or any other programming language+Claude/Codex/any other ) with production-quality code, testing, and documentation; strong SQL and PySpark for billion-row datasets Databricks (nice to have/learn for 2027) 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 (2 questions to level) Proficiency with MLOps practices: experiment tracking and reproducible model deployment Exposure to modern AI methodologies : RAG systems, LLM-augmented models, vector databases, and semantic search (showcase+examples) 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 Team player

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