Samba TV

Delivers TV audience measurement and insights

Data Scientist

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeAmsterdam, NetherlandsNo salary listedIn-person

About the Job

Requirements

A Bachelor's degree in Statistics, Data Science, Computer Science, Mathematics, or a related quantitative field is required; a Master's degree is strongly preferred.
At least 2 years of hands-on data science experience, including independently owning and delivering complex, multi-sprint projects.
Advanced Python, or another programming language used with Claude, Codex, or another such tool, with production-quality code, testing, and documentation; strong SQL and PySpark skills for billion-row datasets.
Working knowledge of Databricks workflows, Delta Lake, job orchestration, and cloud platforms such as AWS or GCP.
Solid command of machine learning, including regression, classification, clustering, model evaluation, and experimental design, applied to complex, high-volume data.
Proficiency with MLOps practices, including experiment tracking and reproducible model deployment.
Exposure to modern AI methodologies, including retrieval-augmented generation systems, large language model-augmented models, vector databases, and semantic search.
Ability 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.

Responsibilities

Own end-to-end delivery of significant data science projects, from problem scoping and approach design through production deployment.
Make independently reasoned decisions about methodology, model selection, and evaluation, and document them in technical solution documents covering the problem statement, approach, metrics, and timeline.
Lead solution design for own initiatives and break complex epics into well-scoped user stories with clear acceptance criteria.
Apply DataOps and MLOps best practices, including experiment tracking, pipeline orchestration, model monitoring, and reproducibility.
Implement machine-learning and AI-powered workflows, including entity resolution, probabilistic record linkage, embedding-based matching, semantic similarity, and large language model-augmented pipelines.
Develop and maintain reusable tools, libraries, and documentation that improve team efficiency and technical standards; conduct code reviews with constructive, specific feedback.
Collaborate with product, engineering, and operations to translate business requirements into technical specifications; partner with data engineering on scalable pipeline design; and participate in cross-functional design reviews and working groups.

Desired Qualifications

Databricks workflows, Delta Lake, and job orchestration are described as nice to have, with an indication they may be learned for 2027.
A Master's degree is strongly preferred.

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.

What You'll Do:

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

Who You Are:

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

Samba is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We strive to empower connection with one another, reflect the communities we serve, and tackle meaningful projects that make a real impact.

Samba may collect personal information directly from you, as a job applicant, Samba may also receive personal information from third parties, for example, in connection with a background, employment or reference check, in accordance with the applicable law. For further details, please see Samba's Applicant Privacy Policy. For residents of the EU , Samba Inc. is the data controller.

About the Company

Samba TV

Delivers TV audience measurement and insights

Samba TV collects and analyzes TV viewing data to help advertisers, broadcasters, and content creators understand what people watch. It embeds its technology in about 35 million smart TVs across 20 brands, creating a large viewership database and a household identity graph. The system works by using a proprietary multi-source panel from participating TVs to gather viewing activity, then turning that data into insights and analytics that clients can use to plan ads, optimize content, and measure performance. What sets Samba TV apart is the size and scope of its data: it claims the largest end-to-end viewership dataset and identity graph in the industry, with a panel hundreds of times larger than typical measurements and broad brand partnerships, while offering privacy controls for viewers who opt in or out. The company aims to provide data-driven, actionable insights to improve advertising effectiveness and content delivery across the global smart TV market.

Company size: 201-500
Company stage: Debt Financing
Total funding: $75.7M
Headquarters: San Francisco, CA
Founded: 2008

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