Junior Data Scientist role in Amsterdam. Own end-to-end delivery of data science projects with minimal guidance. Expertise in identity and audience modeling, building production-ready ML and AI solutions. Collaborate with product and engineering teams. Requirements: Bachelor's degree in quantitative field (Master's preferred), 2+ years data science experience, advanced Python, SQL, PySpark skills, knowledge of Databricks, Delta Lake, AWS or GCP, strong ML fundamentals, MLOps proficiency, exposure to modern AI methods including RAG systems and LLMs, strong communication, mentoring ability, and teamwork.
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
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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
Requirements
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
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
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
