Data Scientist; m​/w​/d

Is this your next level career ambition?

As part of the Computational & Data Science team, you'll work at the crossroads of science, engineering, product development, and business, turning experimental, product, and process data into powerful analytical solutions that enable smarter decision-making.

Our client - A global leader with a strong commitment to power the vehicles of the future hy accelerating the transition to clean mobility & developing breakthrough materials technology.

You - A hands-on (> mid) Data Scientist passionate about AI + Machine Learning eager to leverage statistical modelling and advanced predictive analytics to support faster product development cycles, improved end-to-end model performance, and generate value across the R&D ecosystem. Experienced in iterative delivery environments - Agile, Scrum, Kanban.

Hanau | Full-time | Hybrid (3 days onsite/week) | 12-month assignment | Occasional travel

• Partner with stakeholders to turn business needs and scientific questions into data-driven strategy
• Design, develop, and enhance ML models for catalyst performance prediction
• Apply advanced analytics techniques, exploratory data analysis, feature engineering, model selection, hyperparameter tuning, and model performance assessment
• Document model assumptions, methods, validation results, and recommendations in a reproducible way
• Explore relevant databases and available data sources to understand data structures, completeness, quality, and usability for modelling
• Unlock insights from correlations, patterns, outliers, and potential performance drivers within catalyst, emissions, laboratory, product, and process data
• Develop scalable workflows that support efficient re-use of cleaned datasets
• Work with catalyst, process, product, and laboratory data to identify performance drivers and relevant modelling features
• Help advance the transition from exploratory analysis and prototype models to robust, usable modelling assets for the Project Team
• Communicate insights and recommendations in a clear, compelling way that inspires action across technical and non-technical stakeholders

• Master's degree in Data Science/Statistics/Mathematics/Computer Science/Engineering/Physics, Chemistry/Materials Science or a related discipline
• Around 5 years of expertise in data science, machine learning, statistical modelling, or predictive analytics
• Solid Python programming skills and hands-on experience with pandas, Num Py, scikit-learn, matplotlib
• Proven expertise with database exploration, data cleaning/preparation, exploratory data analysis, and correlation analysis
• Experience in developing and improving predictive models using complex technical or scientific datasets, in working with structured data from industrial, engineering, manufacturing, laboratory, or R&D environments
• Ability to translate analytical findings into clear visualizations, technical documentation, and model interpretation materials

• Exposure to chemical engineering, catalyst development, emissions systems, materials science, automotive R&D, or related industrial research environments
• Experience with supervised learning, ensemble methods, gradient boosting, regression/classification models, Bayesian modelling, hybrid modelling, or physics-informed machine learning
• Knowledge of SQL databases, data pipelines, large datasets, or industrial data platforms
• Familiarity with Azure Machine Learning, Databricks, cloud-based data platforms, or comparable
• Experience with experimental design, laboratory data, process data, or product development data
• A strong foundation in machine learning together with the ability to collaborate closely with domain experts to connect scientific parameters and engineering constraints to ML features and model outputs

• Effective in cross-functional and international R&D environments with the ability to take ownership of assigned analysis and modelling work streams
• Confident in explaining findings, correlations, model outcomes, and limitations to technical and non-technical audiences
• Strong analytical mindset with the ability to connect data, domain knowledge, and business objectives