Applicative Data Scientist for CATIA

Unlock your potential with Dassault Systèmes, a global leader in Scientific Software Engineering as an applicative data scientist in Daegu, K o rea ! About the Role CATIA, a pioneer in 3D CAD and generative design, is pushing the boundaries of how artificial intelligence transforms engineering creativity. We are looking for an Applicative Data Scientist who can bridge the gap between cutting-edge generative AI and the rigorous world of industrial 3D geometry. In this role, you will not just train models – you will embed them into CATIA’s 2D and 3D applications . You will work at the intersection of deep learning, combinatorial optimization, and computational geometry, turning research breakthroughs into practical engineering experiences that empower designers to create complex, manufacturable designs that were previously impossible to achieve manually. Job Description & Responsibilities AI Model Development & Selection Select, adapt, and fine-tune generative AI technologies (diffusion models, GANs, VAEs, and LLMs) to address strategic challenges in generative design and user experience. Train and qualify AI models , ensuring robust performance, scalability, and strict compliance with company-wide cybersecurity policies, as well as industrial regulations and manufacturing standards. Geometric Algorithm Engineering Implement and optimize algorithms for 3D mesh processing, computational geometry, and spatial data structures . Integrate AI-generated outputs into CATIA's applications, ensuring they satisfy engineering and manufacturing requirements such as manufacturability, geometrical constraints and design robustness. Mathematical Optimization & Search Leverage advanced knowledge in Operations Research, Combinatorial Optimization, and Meta-heuristics (e.g., Simulated Annealing, Adaptive Large Neighborhood Search, MIP) to solve complex design-space search problems. Combine generative methods with optimization techniques to propose design alternatives that satisfy multiple, often conflicting, engineering constraints. Knowledge Integration & Documentation Actively learn and incorporate complex CAD/PLM domain knowledge into AI solution design. Document best practices for selecting, deploying, and integrating AI models within engineering workflows, enabling the broader team to adopt AI technologies effectively. Collaboration & Knowledge Sharing Work effectively in a global, multi-site R&D environment. Share knowledge with software engineers, product managers, and domain experts, translating complex AI concepts into actionable engineering insights.