Computational Scientist

About Us At ExxonMobil, our vision is to lead in energy innovations that advance modern living and a net-zero future. As one of the world’s largest publicly traded energy and chemical companies, we are powered by a unique and diverse workforce fueled by the pride in what we do and what we stand for. The success of our Upstream, Product Solutions and Low Carbon Solutions businesses is the result of the talent, curiosity and drive of our people. They bring solutions every day to optimize our strategy in energy, chemicals, lubricants and lower-emissions technologies. We invite you to bring your ideas to ExxonMobil to help create sustainable solutions that improve quality of life and meet society’s evolving needs. Learn more about

our What and our Why and how we can work

together . What You Will Do

• Work collaboratively across global, cross-disciplinary teams, and with third parties (academia, industry) to assess, accelerate pace of computational science technology development and deployment.
• Frame computational challenge from business needs, develop solutions that strike a balance between accuracy and runtimes, develop solutions that merge physics and data incorporating uncertainty, develop novel approaches to constrain predictive models with field data.

Skills And Qualifications

• Master’s or PhD degree from a recognized university in Engineering/Applied Mathematics/Geoscience/Computational Science with a GPA 7.0 and above (out of 10.0).
• For candidates with only master’s degree, minimum 3 years of relevant work experience is required.
• Experience in developing, applying, and analyzing physics-based models and developing related algorithms.
• Strong background in multiscale and/or multiphysics mathematical modeling, scientific computing, and numerical analysis.
• Hands-on experience with physics-based simulators and computational modeling.
• Familiarity with surrogate modeling for optimal design and inverse problem-solving is preferred but not mandatory.
• Exposure to hybrid modeling approaches combining data-driven and physics-based methods using machine learning tools (e.g., TensorFlow, PyTorch, Scikit-learn)
• Knowledge of deep learning techniques for advanced data analysis and modeling challenges is preferred but not mandatory.
• Strong proficiency in programming/scripting languages like C++/C# or Python.
• Experience with software engineering best practices including software testing, agile development, version control, and DevOps.
• Prior experience in the upstream oil and gas industry is an advantage.
• Strong communication skills and ability to work effectively in interdisciplinary teams to translate complex computational models into actionable insights. Functional Skills Continuous Mathematical Optimization Multi scale or Multi physics modeling High Performance Scientific Computing Multi-fidelity AI Surrogates Mathematical Framing of Business Problems Stay Connected With Us
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