Next-Link
Data Scientist
Bruxelles, BEVor OrtUnbefristetVollzeit
Veröffentlicht am 20. Aug. 2026
Diese Stelle wird in EN ausgeschrieben
The Senior Lead Data Scientist will design, develop, and industrialise scalable machine-learning solutions using Azure Databricks, Python, PySpark, MLflow, and Azure services. The role combines advanced data science expertise with technical leadership, working closely with domain experts, data engineers, and architects to deliver robust, production-ready solutions.
Requirements
- Education: Master’s or Ph.D. in Data Science, Computer Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related discipline; equivalent professional experience will also be considered.
- Experience: 10+ years of hands-on experience in data science, machine learning, advanced analytics, or a closely related field.
- Technical Skills: Advanced proficiency in Python and SQL, with strong hands-on expertise in Azure Databricks and PySpark.
- Machine Learning: Extensive knowledge of machine learning methods, including anomaly detection, classification, clustering, explainable AI, and risk-scoring models applied to large-scale datasets.
- Databricks Platform: Proven experience with key Databricks capabilities, including MLflow, Delta Lake, Unity Catalog, and Databricks Workflows.
- Engineering & MLOps: Strong understanding of software engineering and MLOps practices, including Git, automated testing, CI/CD pipelines, model deployment, and lifecycle management.
- Ways of Working: Demonstrated experience delivering solutions within Agile development environments.
- Industry Experience: Background in healthcare, insurance, fraud detection, or the public sector would be highly advantageous.
Rollenübersicht
Jobart
Vollzeit
Erforderliche Kompetenzen
Azure DatabricksPySparkPythonSQLMLflowDelta LakeUnity Catalog (Databricks)Databricks WorkflowsMLOps and CI/CD pipelines (model deployment & lifecycle management)Machine learning methods (anomaly detection, classification, clustering, explainable AI, risk-scoring)Software engineering best practices (Git, automated testing, code quality)Agile developmentTechnical leadership and cross-functional collaborationDomain knowledge in healthcare, insurance, fraud detection, or public sector
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