Air Liquide is looking to solve challenging data science problems focusing on material development, operations, supply chain, and customers
Requirements
- A strong background and experience in statistics and machine learning
- Strong programming skills in Python and scientific libraries
- Experience with data processing and analysis using tools like NumPy, SciPy, and pandas
- Familiarity with version control using git and experience with collaborative code repositories such as GitHub and GitLab
- Knowledge in material science or chemistry (e.g polymer science)
- Experience with deep learning frameworks like PyTorch, TensorFlow, or JAX
- Hands-on experience in modern deep learning architectures (e.g. Transformers) or graph representation learning (e.g., Graph Neural Networks)
Responsibilities
- Collaborate with the business and other R&D research teams to define needs and challenges, translate them into functional specifications, and develop solutions to address them
- Design and execute experiments or studies to investigate scientific questions or problems, e.g. build and refine machine learning and deep learning solutions to accelerate material design and model development
- Lead data collection effort, develop solutions relying on machine learning methods, including deep learning and Generative AI
- Test and verify the performance of solutions with prototypes developed in Python or similar development environments
- Support the development of industrial tools and their deployment in the business units
- Prepare detailed technical reports summarizing research methodologies, results, and implications
- Publish research in internal reports, at conferences and peer-reviewed journals
Other
- M.S or Ph.D. in Computer Science, Statistics, Chemical Engineering, Physics or related field
- Demonstrates strong process and operational safety behavior at all times
- Analytical and problem-solving skills
- Excellent communication and interpersonal skills (written and oral)
- Ability to work in a multi-disciplinary and international team
