Senior AI Scientist — Production ML & Data Systems

Samba; Amsterdam; Hybrid; EUR 110,000 - 160,000; Full time

Job summary

Samba is seeking a senior data scientist to shape its data science function, define modeling methodologies, and build production ML systems at scale. You will collaborate with engineering, product, and partner teams to implement rigorous, defensible modeling practices and drive data-driven decision making.

Responsibilities cover end-to-end delivery, building AI systems with RAG/LLM approaches, integrating agentic workflows, and leading technical design while mentoring junior data scientists.

Qualifications

• 8+ years of hands-on data science experience with a quantitative degree or equivalent
• Demonstrated ability to own multi-sprint projects from scoping to production deployment
• Strong foundation in ML, statistics, and evaluation on large real-world datasets

Responsibilities

• Own end-to-end delivery of data science projects from scoping to deployment
• Define and ship modeling methodology and evaluation frameworks
• Partner with Data Engineering to validate pipelines and ensure reliable inputs
• Build AI systems using RAG, LLM-augmented modeling, and Graph Neural Networks
• Integrate LLMs and agentic workflows into production ML pipelines
• Drive technical design for modeling components and mentor data scientists
• Establish and operate MLOps practices including experiment tracking, orchestration, monitoring, and governance

Skills

Leadership

Communication

Production-grade Python

ML & statistics

Education

Bachelor's in Statistics/DS/CS/Math

Master's preferred

Tools

Databricks

Delta Lake

Airflow

AWS/GCP

SQL

Job description

Samba is seeking a senior data scientist to shape its data science function, define modeling methodologies, and build production ML systems at scale. You will collaborate with engineering, product, and partner teams to implement rigorous, defensible modeling practices and drive data-driven decision making.

Responsibilities cover end-to-end delivery, building AI systems with RAG/LLM approaches, integrating agentic workflows, and leading technical design while mentoring junior data scientists.