Senior Data Scientist role in Amsterdam to lead data science projects from problem scoping to production deployment. Responsibilities include defining modeling methodology, building production ML systems using Python and PySpark on Databricks, applying ML and statistics to large datasets, collaborating with engineering and product teams, and mentoring junior data scientists. Requires 8+ years experience, expertise in ML, AI, vector databases, MLOps, and production deployment. Must have strong skills in Python, SQL, PySpark, Databricks, Airflow, AWS, and GCP. Experience with agentic AI systems and privacy-compliant data handling (GDPR, CCPA) is required. Benefits include bonuses, health insurance, wellness offerings, life and disability insurance, retirement plan, 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
Clear communicator who translates technical work into design docs, user stories, and cross-functional conversations
Active mentor who invests in others, gives direct feedback, and raises the bar for the team
