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Tier-1 brand and Bangalore metro increase applicant density despite niche scientific-ML specialization.
Highly domain-specific Scientific ML and Multiphysics expertise limits cross-industry transferability.
Requires PhD and demonstrable Scientific ML plus domain expertise, creating stringent screening criteria.
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Develop and deploy physics-guided AI and Scientific Machine Learning (ML) solutions for complex Multiphysics engineering problems across multiple Shell businesses (e.g., Low Carbon Fuels, CCS, Upstream).
Act as a technical integrator selecting and designing appropriate Scientific ML approaches based on business needs, ensuring model robustness, validation, and operational relevance.
Drive R&D in AI-accelerated simulations and model performance optimization, contribute to standards, and maintain lifecycle management of Scientific ML models.
PhD or equivalent experience in Applied Mathematics, Computational Physics, Computational Engineering, or AI/Machine Learning.
Deep hands-on experience with Scientific ML methods such as Physics-informed learning, hybrid modeling, neural operators, or uncertainty quantification.
Proven application of AI/Scientific ML to complex, real industrial systems with ability to work with domain experts under practical constraints.
Strong programming skills (Python and modern ML frameworks) and background in major Multiphysics or engineering domains like CFD, structural or thermal analysis, electrochemistry, or materials modelling.
Experienced individual contributor comfortable blending deep AI algorithmic innovation with broad engineering judgment and business understanding.
Capable of navigating incomplete physics, sparse or biased data, and operational constraints to deliver scalable, practical AI solutions in asset-intensive industries.
Experienced in multidisciplinary, research-to-deployment environments with strong collaboration across domain experts and stakeholders.