Digital Transformation & AI Scientist

Job description

Job Title: Digital Transformation & AI Scientist Job Description: Job Title: Digital Transformation & AI Scientist  Job Summary  The mission of this role is to drive digital transformation across engineering workflows by designing, developing, and deploying advanced computational solutions that integrate modeling & simulation, scientific machine learning, and physics-based approaches.  The Digital Transformation and AI Engineer will focus on building scalable systems that combine engineering simulations, data-driven models, and domain knowledge to enable smarter design, analysis, and decision-making. As part of this transformation roadmap, the role will contribute to the development of digital twins, along with next-generation capabilities in scientific ML, multiphysics modeling, and hybrid physics–AI systems.  This role is hands-on, implementation-focused, and ideal for someone passionate about modeling & simulation, scientific machine learning, and applied physics-driven AI. The position requires working across fluid, thermal, and structural domains, bridging engineering fundamentals with modern AI techniques.  Reports to: Sr Manager, Modeling and Simulation   Key Responsibilities  • Design and develop digital twin frameworks for engineering systems, integrating simulation, data ingestion, and analytics   • Build and maintain pipelines that couple CFD, thermal, and structural simulations with AI/ML models   • Develop physics-based surrogate models using machine learning techniques such as regression, reduced-order modeling, and neural networks   • Apply scientific machine learning approaches, including physics-informed neural networks (PINNs) and hybrid modeling strategies   • Integrate simulation outputs with data pipelines for calibration, validation, and continuous improvement of digital twins   • Develop Python-based services and tools to orchestrate simulation workflows, preprocessing, and postprocessing   • Work with domain experts to translate engineering problems into computational models and scalable digital twin architectures   • Implement model validation, uncertainty quantification, and performance benchmarking for simulation and surrogate models   • Create visualizations to analyze physical behavior, simulation outputs, and model predictions   • Deploy digital twin solutions into production environments, ensuring scalability, reliability, and integration with enterprise systems   • Document systems and contribute reusable components to internal modeling and AI frameworks  Requisite Criteria & Skills  • PhD degree (minimum) in Mechanical Engineering, Aerospace Engineering, Computer Science, or a related engineering discipline  • 3&#43; years in modeling & simulation, scientific computing, AI/ML, or applied research roles  • Experience developing or using </sp