Data Scientist

Company Description The Bosch Group has more than 400 000 employees around the world, present in 60 countries, and we are proud to impact people’s lives and to work towards a more sustainable future. Bosch Car Multimedia, S.
A. belongs to the Automotive Electronics division and is focused on making the vehicles our third living space. About 3.600 associates are committed to develop and produce high quality technology that shape change in mobility worldwide. The company' success lies in its highly specialized and innovative team, and on the technological know-how which makes Bosch the leading supplier in the automotive market. At Bosch, we shape the future by inventing high-quality technologies and services that spark enthusiasm and enrich people’s lives. Our promise to our associates is rock-solid: we grow together, we enjoy our work, and we inspire each other. Join in and feel the difference in mindsets, cultures, generations, identities and perspectives. Everyone should bring their authenticity and work together respectfully. Bosch is an employer that values diversity and equal opportunities. We welcome applications from people with disabilities and we can provide reasonable accommodations during the recruitment process and in the performance of professional activity. By including everyone and ensuring equal opportunities we unleash our full potential.

Job Description
As a Data Scientist, you will help shape the future of manufacturing by transforming complex engineering and production challenges into data-driven solutions that create measurable business value. Working at the intersection of Artificial Intelligence, Machine Learning and Industrial Analytics, you will analyse manufacturing data to uncover patterns, identify root causes and drive process improvements. Your insights will support smarter decisions, enhance product quality and increase operational efficiency across manufacturing environments. You will develop and validate AI/ML models and advanced analytics solutions for use cases such as:
- Quality improvement and process optimization
- Anomaly detection and root cause analysis
- Predictive maintenance and failure prediction
- Productivity and yield improvement
- Process stability monitoring
- Decision support for engineering and manufacturing teams As part of a multidisciplinary team, you will collaborate closely with Process Experts, Technology Development Engineers, Data Analysts, MES/PLC specialists, Data Engineers, MLOps teams, IT professionals and business stakeholders to ensure solutions are scalable, explainable and aligned with real operational needs. You will contribute throughout the entire solution lifecycle, from problem definition and data exploration to model development, validation, deployment support and continuous improvement. In addition, you will help standardize and reuse analytical methods, models and best practices across projects and locations, supporting Bosch's digital transformation journey and Industry 4.0 initiatives. Qualifications What distinguishes you: Education
- MSc or PhD in Data Science, Artificial Intelligence, Machine Learning, Computer Science, Mathematics, Statistics, Engineering, Physics or a related field.
- Academic or professional background in machine learning, statistics, optimization or data-driven problem solving. Experience
- Experience developing Machine Learning, Artificial Intelligence or Advanced Analytics solutions.
- Hands-on experience working with complex datasets in industrial, engineering, manufacturing or technical environments.
- Experience contributing to end-to-end data science projects, including data preparation, model development, validation and deployment.
- Exposure to manufacturing, process development, industrialization, quality improvement or equipment-related analytics is considered an advantage. Know-how 1. Machine Learning & Advanced Analytics
- Knowledge of machine learning methods, including regression, classification, clustering, anomaly detection, forecasting and optimization.
- Experience applying techniques such as feature engineering, model evaluation, hyperparameter tuning and model explainability.
- Familiarity with deep learning approaches is a plus. 2. Statistics & Experimental Methods
- Knowledge of statistical analysis, probability and experimental data analysis.
- Experience with hypothesis testing, regression analysis, statistical significance and data interpretation.
- Understanding of process variability, capability analysis, performance indicators and Design of Experiments (DoE). 3. Programming & Data Science Tools
- Proficiency in Python and good knowledge of SQL.
- Experience with data science and machine learning libraries such as Pandas, NumPy, Scikit-learn, SciPy, Matplotlib, Seaborn, Plotly, PyTorch or TensorFlow.
- Familiarity with Git and collaborative software development practices. 4. MLOps & Industrial Deployment
- Understanding of MLOps concepts, incl