Build and operate ML models and data science workflows that support analytics, optimization, and AI-driven use cases
Design, develop, and deploy scalable ML pipelines on Azure Databricks
Manage the full model lifecycle using MLflow, Databricks Model Registry, and Feature Store
Deploy and monitor ML models at scale in production environments
Collaborate with interdisciplinary teams to translate business needs into data-driven, AI-powered solutions
Continuously improve data science and MLOps practices, tooling, and processes to support innovation and scalability

A completed degree in Computer Science, Business Informatics, Industrial Engineering, or a comparable engineering or natural sciences field
Professional experience in the design and implementation of data/ML pipelines and production ML environments
Hands-on experience with Azure Databricks (2+ years in production environments), including: MLflow integration; Databricks Model Registry and model serving, feature Store
Experience deploying ML models at scale
A solid understanding of business domains and processes within a large enterprise, with the ability to translate business needs into data-driven solutions
Experience working in interdisciplinary teams and collaborating effectively across functions
Strong knowledge of Python and the Python data science ecosystem (e.g., pandas, NumPy, scikit-learn, PySpark)
A solid foundation in statistics, machine learning, feature engineering, and model evaluation
Data engineering awareness: stream/batch processing, data quality, Medallion Architecture, and orchestration

Professional development
Continuous improvement and optimization in ML/data science practices
Collaborative attitude and enthusiasm for dynamic, cross-functional environments

Data Engineer, Databricks

Key skills