Job Description
- 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 standardsQualifications
- 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