Develop and deliver robust machine learning solutions addressing diverse business challenges (forecasting, classification, optimization, automation) on the Azure Databricks platform.
Own the full ML lifecycle: model development, deployment, monitoring, and retraining — supported by standardized infrastructure and DevOps practices.
Apply strong mathematical and problem-solving skills to translate complex business requirements into effective ML models.
Collaborate with Product Owners, data engineers, DevOps, and architecture teams to build scalable, maintainable, and governed ML pipelines.
Demonstrate curiosity and an iterative mindset, exploring alternative modeling approaches to achieve satisfactory business outcomes.
Reports to: Head of Data & Analytics IT Competence Center
Collaborates with: Product Owners, data engineers, DevOps engineers, architecture/governance teams
Location scope: Global business and IT teams
Platform scope: Databricks (MLflow, notebooks, jobs, model registry), Azure services (Blob Storage, Key Vault, Event Hub, API Management)
Main Tasks
- Design, build, and evaluate ML models primarily in Python using libraries such as scikit-learn, XGBoost, Prophet, PyTorch, TensorFlow
- Perform feature engineering using pandas and PySpark where needed
- Collaborate with data engineers on data acquisition and pipeline integration
- Package and deploy models to production using MLflow’s Python API and CI/CD pipelines
- Manage model versioning, monitoring, and lifecycle workflows
- Build retraining pipelines and schedule model refreshes
- Integrate ML workflows with Azure-native services (Functions, Event Grid, API Management)
- Collaborate with DevOps engineers to automate deployments and enable observability
- Align with architecture and governance teams on standards compliance
- Advise Product Owners and business teams on feasibility, complexity, and architectural implications of ML solutions
- Translate business problems into viable ML models and workflows
- Support backlog prioritization and iterative development
- Write clean, reusable, testable code for ML pipelines using software engineering best practices
- Contribute to shared libraries and reusable components
- Apply version control, testing, and documentation standards
Education / Certification:
Degree in Computer Science, Data Science, Engineering, Mathematics, or related field
Preferred certifications in Azure Data & AI, Databricks, or MLflow
Professional Experience:
3–5+ years of hands-on experience in applied machine learning, developing production-grade models for business use cases
Project or Process Experience:
Proven ability to translate business challenges into effective ML models, conduct experimentation, and iterate toward impact
Experience working with large-scale structured data and integrating models into data pipelines
Leadership Experience:
No direct management responsibilities; expected to act as technical lead for ML within product teams
Intercultural / International Experience:
Experience collaborating with globally distributed and cross-functional teams
The well-being of our employees is important to us. That's why we offer exciting career prospects and support you in achieving a good work-life balance with additional benefits such as:
Training opportunities
Mobile and flexible working models
Sabbaticals
and much more...
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are important to us and make our company strong and successful. We offer equal opportunities to everyone - regardless of age, gender, nationality, cultural background, disability, religion, ideology or sexual orientation.
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