Digital Transformation & AI Scientist

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+ years in modeling & simulation, scientific computing, AI/ML, or applied research roles 

• Experience developing or using surrogate models or reduced-order models 

• Strong proficiency in Python programming 

• Hands‑on experience with CFD, thermal, and/or structural simulation workflows (FEA) 

• Familiarity with scientific machine learning techniques and physics-based modeling approaches  

• Ability to independently design, build, and debug end-to-end simulation or digital twin pipelines 

• Practical experience with machine learning libraries, including NumPy, Pandas, SciPy, scikit‑learn, TensorFlow and/or PyTorch 

• Ability to independently design, build, and debug end‑to‑end AI workflows  

Success will be measured by:  

• Effectiveness and accuracy of developed digital twin systems in representing real-world behavior  

• Performance gains achieved through surrogate modeling or hybrid simulation approaches  

• Quality, scalability, and maintainability of implemented simulation and AI pipelines  

• Demonstrated impact on engineering productivity, design optimization, or decision-making  

• Ability to translate complex physical problems into robust computational solutions  

• Strong collaboration with engineering, simulation, and research teams 

• Further the candidate will need to interact with people from all levels of experience within Entegris Business units and external research institutes. The candidate not only needs to demonstrate innovation and creativity but also needs to be accountable for project deliverables and commitments. To ensure success and excellence the candidate needs to consistently set high standards for oneself for best-in-class outcomes in all areas of work