Senior Data Scientist role in Amsterdam focused on defining modeling methodology, building production ML systems, and applying modern AI and agentic capabilities across data products. Requires 8+ years experience with machine learning, AI, and production deployment. Responsibilities include end-to-end project delivery, methodology development, building production-quality Python and PySpark code on Databricks, collaborating with engineering and product teams, mentoring, and establishing MLOps practices. Must have experience with vector databases, MLops, cloud platforms (AWS, GCP, Databricks), and privacy-compliant data handling (GDPR, CCPA). Benefits include bonuses, health insurance, wellness, life and disability insurance, retirement savings, paid holidays, and PTO.
What you'll do
Own end-to-end delivery of data science projects, from problem scoping through production deployment.
Define and ship modeling methodology that powers Samba's data products, including model selection, evaluation frameworks, and reproducibility standards.
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Apply privacy-compliant data handling practices, including GDPR, CCPA, and Samba's data governance policies.
Requirements
8+ years of hands-on data science experience with a Bachelor's degree in Statistics, Data Science, Computer Science, Mathematics, or a related quantitative field (or 6+ years with a Master's, 3+ years with a PhD, or equivalent).
Demonstrated ability to own and deliver complex, multi-sprint data science projects from problem scoping through production deployment.
Solid command of core ML and statistics, including neural networks, regression, classification, clustering, model evaluation, experimental design, and causal inference, applied to billion-row datasets.
Track record of building methodology, not just applying it: data analysis, model selection, evaluation frameworks, and solid documentation of decision processes.
Production experience with vector databases (Pinecone, Weaviate, Milvus, pgvector, or equivalent) for retrieval, matching, or inference at scale.
Advanced Python with production-quality, tested code; strong SQL and PySpark on billion-row datasets.
Databricks, Delta Lake, and job orchestration (Airflow); hands-on production experience on AWS, GCP, and Databricks.
MLOps proficiency: experiment tracking, pipeline orchestration, model monitoring, reproducible deployment.
Experience designing and operating agentic AI systems in production: prompt engineering, agent orchestration, tool use, or integration of LLMs into ML pipelines.
A clear communicator who translates technical work into design docs, user stories, and cross-functional conversations.
An active mentor who invests in others, gives direct feedback, and raises the bar for the team as a whole.
