Airframe Data Scientist - Robust AI/ML

Job Description: Job description: Airframe Data Scientist - Robust AI/ML Team: Structures – Aerostructure Data Management Background:   Designing, optimizing, and validating robust airframe structures is a fundamental aspect of aviation that ensures aircraft safety and performance. The Advanced Capabilities cluster in Aerostructure Data Management team focuses on bridging traditional structural engineering with advanced digital technologies including artificial intelligence. By integrating physics-based modeling with cutting-edge data-driven approaches, the team aims to enhance the airframe lifecycle, streamline structural analysis, and extract actionable insights from historical and operational data. Role:   The candidate will act as a Data Scientist / Machine Learning Engineer focusing on Airframe-related applications with the task to provide safe, robust, and balanced data-driven/hybrid design solutions. She/He will play a key role in developing and deploying advanced AI solutions that augment and accelerate structural engineering. Key activities include: Developing Surrogate Models to accelerate structural simulations. Implementing Hybrid Modelling (physics + data) and Scientific Machine Learning techniques. Exploring and applying Large Language Models (LLMs) to assist in Finite Element Analysis (FEA) workflows. Utilizing Computer Vision and AI techniques for automated CAD/mesh quality inspection. Engaging in data engineering and knowledge graph creation from unstructured legacy documents to preserve and utilize engineering know-how. Ensuring rigorous Verification, Validation, and Uncertainty Quantification (VVUQ) of AI/ML solutions deployed within the Airframe perimeter. Technical Skills  Key Skills   Machine learning and Deep learning code development and deployment  Expertise in Surrogate Modelling, Hybrid Modelling (physics + data), and Scientific Machine Learning  Experience in Computer Vision (e.g., for CAD/mesh inspection), Natural Language Processing & Generative AI (Knowledge Graph creation, Semantic reasoning, unstructured data extraction etc.)  Solid background in Verification, Validation, and Uncertainty Quantification (VVUQ) for AI/ML models Application development in environments like AWS, GCP, Skywise and on-prem platforms.  Data-driven decision science Good to have (not mandatory)   Basic understanding of Structural Engineering, Finite Element Analysis (FEA), and Aerospace/Aviation  Basic Knowledge of Generative Artificial Intelligence, LLMs for engineering applications, and state-of-the-art modeling techniques  Basic Knowledge on Large Language Model development, deployment, and monitoring  Exposure to Software development, testing, verification, and integration Preferred Language: Python, Julia, C#, JavaScript Data Science Frameworks: Scikit-Learn, Keras, Tensorflow, Pytorch, Langchain, FEniCS etc.   Environments: AWS, GCP or similar platforms. Soft Skills: