ExxonMobil recruits a Mathematical Optimization Research Scientist – Intern, Spring, TX, US

Join ExxonMobil as a mathematical optimization scientist intern/co-op with background in industrial engineering, chemical engineering, applied mathematics or other engineering disciplines with optimization skills. Become part of an interdisciplinary team to develop, deploy and sustain solutions for the upstream, downstream or chemical businesses. You will work to apply various operations research technologies to support critical business decisions across the entire value chain. The focus on value generation provides the opportunity to have significant impact leveraging ExxonMobil’s strong belief in creating commercial advantage through the use of technology. The company places high emphasis on fundamental scientific skills and the ability to combine them with business understanding to improve profitability.

• Work under the guidance of a mentor to design and develop complex simulation and optimization models and apply them to define strategic and tactical needs and drive the appropriate business and technical solutions.
• Apply theories of mathematical optimization, including linear programming, combinatorial optimization, integer programming, network flows and algorithms to design optimal or near optimal solution methodologies.
• Provide technical support and enhancements to affiliates worldwide for deployed research applications.
• Summarize results and develop technical reports and presentations.

• Ph.D. Candidate in Engineering, Applied Mathematics, Computer Science, Operations Research, or other related scientific disciplines, combined with strong mathematical programming and algorithmic skills.
• Strong background in modeling large scale discrete, nonlinear, or stochastic mathematical optimization models.
• Strong background in developing efficient optimization algorithms.
• Expert in at least one major programming language (C/C++, C#, Java, Python, etc.) or optimization modeling language (e.g. AMPL, GAMS, AIMMS, OPL, etc.).