Computational Scientist
Posted 27 Sep 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 3+ yrs
- Management level
- Individual contributor
No salary listed for this role.
Field of studyApplied Mathematics, Computational Science, Engineering, Geoscience
Study levelMaster's or higher
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.