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Senior Data Scientist

TomTom

TomTom

Data Science
Amsterdam, Netherlands
Posted on Dec 5, 2025
We are looking for a Senior Data Scientist who can build end-to-end data products, work across both data science and data engineering, and contribute to our architecture while exploring new AI technologies such as MCP and AI Agents
What you will do:
  • Design and develop scalable data science solutions from data ingestion to deployment.
  • Build and maintain data pipelines using Databricks, Spark, and Azure services.
  • Develop, deploy, and monitor machine learning models in production environments.
  • Contribute to architecture decisions and drive improvements in engineering best practices.
  • Integrate modern AI technologies, such as MCP and AI Agents, into data workflows.
  • Lead problem-solving for complex technical challenges across data and ML systems.
  • Mentor junior team members and support their technical growth.
  • Own and deliver medium to large data products with minimal guidance.
  • Collaborate with internal and external stakeholders across technical and non-technical teams
What you will need:
  • 6+ years of experience in Data Science or a related field.
  • Strong expertise in machine learning, model development, and production deployment.
  • Experience with Databricks, Spark, and building scalable data pipelines.
  • Working knowledge of Azure cloud services and infrastructure.
  • Ability to design performant, maintainable, and well-tested software solutions.
  • Strong problem-solving skills with the ability to handle complex technical challenges.
  • Experience breaking large tasks into clear deliverables and planning work independently.
  • Ability to communicate clearly with cross-functional teams
Nice to have:
  • Experience with Model Context Protocol (MCP) for integrating AI into tooling.
  • Understanding of AI Agents and agent frameworks.
  • Experience with CI/CD for data and ML pipelines.
  • Background in mentoring teams or supporting hiring processes.
  • Familiarity with operational excellence practices for ML systems
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