Data Scientist – Chemistry and Materials Science- IBM FNC - H/F

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

Your Role And Responsibilities

We are seeking a Data Scientist with a PhD-level background in materials science, chemistry, or chemical engineering. This role is ideal for scientists who enjoy computational work, have experience with data‑driven or simulation-driven research, and want to apply AI to accelerate discovery and innovation at enterprise scale.

You will combine your scientific expertise with advanced data science and machine learning techniques to model materials, analyze experimental datasets, predict properties, optimize formulations, and support generative design. You will work closely with IBM Research, academic collaborators, and industry R&D teams to translate deep scientific knowledge into deployable AI workflows and scientific intelligence solutions

Preferred Education

Doctorate Degree

Required Technical And Professional Expertise

• Develop machine learning and generative AI models for materials and chemical applications, including structure–property prediction, molecular and materials generation, formulation optimization, and process modeling.
• Apply scientific knowledge to design, curate, and analyze complex datasets from experiments, simulations, spectroscopy, microscopy, computational chemistry, or materials characterization.
• Integrate ML workflows with scientific computing tools such as molecular dynamics, DFT, phase-field modeling, or multi-scale simulation frameworks.
• Collaborate with IBM Research on science foundation models and domain‑specific AI methods (material models, chemistry models, formulation models).
• Extract scientific knowledge from literature, patents, and experimental reports using knowledge graphs and document-understanding technologies.
• Validate AI‑generated hypotheses with subject-matter experts and guide experiment planning and design space exploration.
• Work directly with industry R&D clients to identify scientific challenges, design AI-enabled solutions, and communicate results.
• Contribute to Science Next platform components including data products, virtual models, agentic scientific workflows, and scientific foundation models.

• French language is a must.
• PhD in Materials Science, Chemistry, Chemical Engineering, Physical Chemistry, Polymer Science, Nanoscience, or a closely related field.
• Strong theoretical understanding of materials or chemical systems, including structure–function relationships, thermodynamics, kinetics, or molecular interactions.
• Proficiency in Python and scientific computing libraries (NumPy, SciPy, Pandas, scikit-learn).
• Experience applying machine learning to scientific problems (materials property prediction, spectroscopy analysis, molecular modeling, structure generation, or simulation data analysis).
• Familiarity with cheminformatics or materials informatics libraries (RDKit, pymatgen, ASE, Matminer).
• Experience working with simulation tools such as MD, DFT, Monte Carlo, or finite element modeling.
• Ability to work in interdisciplinary teams and to communicate complex scientific and computational concepts clearl

• Experience with generative AI, deep learning, or foundation models in scientific domains.
• Hands-on experience with materials design, formulation science, polymers, batteries, catalysis, or advanced materials R&D.
• Understanding of high-performance computing environments or cloud computing.
• Experience with literature mining, knowledge graphs, or large scientific text corpora.
• Track record of peer-reviewed publications, patents, or contributions to open scientific datasets or software.