Data Scientist (f/m/div.)

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. 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, including model versioning, testing, reproducibility, CI/CD and monitoring. • Experience with cloud-based AI platforms, preferably Databricks, is an advantage. • Knowledge of APIs, containers and deployment concepts is b