The role
03This 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.
01Lead Data Science & AI initiatives in Science and Technology (S&T) scope, by combining advanced data science leadership with deep scientific domain understanding
02Provide guidance and support to Data Scientists and AI Engineer in the design and development of complex data models, algorithms, and scientific data analysis approaches that enhance decision-making and business outcomes
03In charge of the planning and iterative delivery of Data Science & AI solutions to deliver robust and actionable products for Science and Technology
04Contribute to the definition and improvement of Data Science & AI methodologies, including scientific modeling and experimental data interpretation frameworks.
05Lead the execution and delivery of data science projects, including scientific and chemistry-related initiatives (e.g., research and discovery, analytical chemistry, innovation & knowledge management, …)
06Ensure delivery meets quality, regulatory, and methodology standards, including traceability and reproducibility of scientific results
07Guide the design and implementation of advanced statistical models, machine learning algorithms, and data mining techniques to scientific and chemistry related initiatives including chemical processes and formulations as well as experimental and laboratory data.
08Integrate domain knowledge (chemistry or related fields) into Data Science and AI modeling approaches
09Oversee the analysis of experimental, laboratory and process data (e.g., spectroscopy, chromatography, reaction data)
10Ensure sound scientific interpretation of model outputs, aligned with chemical principles and industrial constraints
11Monitors and evaluates the performance of data science initiatives, using metrics and KPIs to assess impact and identify areas for improvement
12In compliance with global enterprise data governance standards, contributes to the implementation of data governance practices, ensuring data quality, integrity, and compliance across science and technology datasets
13Works 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
