• 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, Py Torch, Tensor Flow

• Perform feature engineering using pandas and Py Spark 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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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.

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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 Conti Tech 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. Conti Tech's industrial growth areas are primarily in the areas of energy, agriculture, construction, and surfaces. In addition, Conti Tech serves the automotive and transportation industries as well as rail transport.

The IT Digital and Data Services Competence Center of Conti Tech caters to all the Business Areas in Conti Tech 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.