Computational Scientist

What You Will Do

• Develop and apply hybrid models that combine first-principles understanding (chemistry, materials behavior) with data-driven methods to accelerate product development.
• Analyze experimental and field data to uncover relationships between formulation, processing conditions, and product performance.
• Build predictive models to support:
  • Formulation optimization
  • Performance prediction
  • Product stability and robustness
  • Quality improvement
• Design and support experiments (DOE) to efficiently generate data for model development and validation.
• Collaborate closely with R&D scientists, chemists, and product developers to guide formulation strategies and decision-making.
• Develop data-driven insights and digital tools to reduce development cycle time and improve success rates.
• Ensure model validity through proper validation, uncertainty quantification, and alignment with domain knowledge.
• Document methodologies, assumptions, and modeling workflows to support reproducibility and knowledge sharing.
• Contribute to development of reusable modeling frameworks and best practices for data-driven product innovation.

About You

• Strong foundation in hybrid modeling, integrating domain knowledge (chemistry, formulations, materials behavior) with statistical and machine learning approaches.
• Experience applying modeling to product development, formulation optimization, and performance prediction.
• Solid understanding of experimental data (lab, pilot, and field) and how to incorporate it into predictive models.
• Proficiency in Python or other programing languagesfor data analysis, modeling, and automation.
• Experience with time-series and experimental datasets related to product performance, stability, and quality.
• Demonstrated ability to translate complex modeling results into insights that guide product design and development decisions.
• Strong communication skills and ability to collaborate with scientists, chemists, and product developers.
• Familiarity with SQL and modern data platforms (Databricks, Azure, AWS, Snowflake) is a plus.
• Ability to work independently and within cross-functional R&D teams.

Skills/Qualifications 

• PhD or Master’s degree in Chemical Engineering, Materials Science, Mechanical Engineering, Data Science, or a related field.
• For Master’s candidates, minimum 3+ years of industry experience required; experience in downstream, chemical, or materials/product development environments is strongly preferred.
• Strong preference for candidates with experience in:
• Formulation modeling or property prediction
• Hybrid modeling applied to product or material performance

Your Benefits

EEO Statement

Nothing herein is intended to override the corporate separateness of local entities. Working relationships discussed herein do not necessarily represent a reporting connection, but may reflect a functional guidance, stewardship, or service relationship. 

Exxon Mobil Corporation has numerous affiliates, many with names that include ExxonMobil, Exxon, Esso and Mobil. For convenience and simplicity, those terms and terms like corporation, company, our, we and its are sometimes used as abbreviated references to specific affiliates or affiliate groups. Abbreviated references describing global or regional operational organizations and global or regional business lines are also sometimes used for convenience and simplicity. Similarly, ExxonMobil has business relationships with thousands of customers, suppliers, governments, and others. For convenience and simplicity, words like venture, joint venture, partnership, co-venturer, and partner are used to indicate business relationships involving common activities and interests, and those words may not indicate precise legal relationships.