Data Science Lead

This role oversees the development and delivery of AI and data science solutions for scientific and chemistry-related applications, including research, laboratory, and industrial R&D environments. Acting as a bridge between Data Science and S&T teams, the role ensures robust scientific interpretation, high-quality data governance, and the integration of machine learning and advanced modeling into research and operational workflows. In this role you will report to the Data Science Manager.

• Lead Data Science & AI initiatives in Science and Technology (S&T) scope by combining advanced data science leadership with deep scientific domain understanding.
• Provide guidance and support to Data Scientists and AI Engineers in the design and development of complex data models, algorithms, and scientific data analysis approaches that enhance decision-making and business outcomes.
• Plan and iteratively deliver Data Science & AI solutions to provide robust and actionable products for Science and Technology.
• Contribute to the definition and improvement of Data Science & AI methodologies, including scientific modeling and experimental data interpretation frameworks.
• Lead the execution and delivery of data science projects, including scientific and chemistry-related initiatives such as research and discovery, analytical chemistry, innovation, and knowledge management.
• Ensure delivery meets quality, regulatory, and methodology standards, including traceability and reproducibility of scientific results.
• Guide the design and implementation of advanced statistical models, machine learning algorithms, and data mining techniques for scientific and chemistry-related initiatives, including chemical processes, formulations, and experimental and laboratory data.
• Integrate domain knowledge (chemistry or related fields) into Data Science and AI modeling approaches.
• Explore and apply knowledge of existing and emerging data science principles, theories, and techniques on model development and evaluation.
• Oversee the analysis of experimental, laboratory, and process data (e.g., spectroscopy, chromatography, reaction data).
• Ensure sound scientific interpretation of model outputs, aligned with chemical principles and industrial constraints.
• Support teams in translating scientific hypotheses into testable data science solutions.
• Monitor and evaluate the performance of data science initiatives, using metrics and KPIs to assess impact and identify areas for improvement.
• Contribute to the implementation of data governance practices, ensuring data quality, integrity, and compliance across science and technology datasets in line with global enterprise data governance standards.
• Ensure adherence to regulatory and industry standards (e.g., traceability, auditability, GxP when applicable).
• Work closely with S&T scientists and business stakeholders to identify AI and data science needs, integrate AI and Data Science into R&D and industrial workflows, enable data-driven scientific decision-making solutions, and support data-driven decision-making.
• Act as a bridge between Data Science and Science & Technology experts.