Your tasks
- 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
Your profile
- 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
Our offer
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...
Sounds interesting for you? Click here to find out more.
Diversity, Inclusion & Belonging 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.
Ready to drive with Continental? Take the first step and fill in the online application.
About us
Continental develops pioneering technologies and services for sustainable and connected mobility of people and their goods. Founded in 1871, the technology company offers safe, efficient, intelligent, and affordable solutions for vehicles, machines, traffic and transportation. In 2023, Continental generated sales of €41.4 billion and currently employs around 200,000 people in 56 countries and markets.
Guided by the vision of being the customer's first choice for material-driven solutions, the ContiTech group sector focuses on development competence and material expertise for products and systems made of rubber, plastics, metal, and fabrics. These can also be equipped with electronic components in order to optimize them functionally for individual services. ContiTech's industrial growth areas are primarily in the areas of energy, agriculture, construction, and surfaces. In addition, ContiTech serves the automotive and transportation industries as well as rail transport.
The IT Digital and Data Services Competence Center of ContiTech caters to all the Business Areas in ContiTech and responsible among other on areas of Data & Analytics, Web and Mobile Software Development and AI
The team for Data services specializes in all platforms, business applications and products in the domain of data and analytics, covering the entire spectrum including AI, machine learning, data science, data analysis, reporting and dashboarding.
