Advisor - Computational Modeling Engineer

Position Description and Responsibilities

The Advisor - Computational Modeling will provide diverse modeling expertise and develop computational models that drive decisions to design new manufacturing processes and improve existing ones across Lilly's global manufacturing network. This role applies modeling techniques across a broad range of domains — including physical properties, unit operations, scale-up, numerical and multiphysics simulation, discrete event simulation, and resource and operational logistics optimization — to support both continuous/batch process operations and discrete manufacturing operations such as parenteral fill/finish, device assembly, and packaging.

A core focus of the role is the development of Digital Twin computational models that provide online modeling capabilities to manufacturing facilities, in alignment with the Engineering Digital Strategy for Manufacturing and Quality within Lilly. This is a global role, supporting functional initiatives and all Lilly Manufacturing sites globally. This role is a technical engineering role at the R4–R6 level on the Tech Ladder, requiring a minimum MS degree in Chemical Engineering, Mechanical Engineering, Industrial Engineering, Operations Research, or a related field.

Basic Qualifications

• Minimum MS degree in Chemical Engineering, Mechanical Engineering, Industrial Engineering, Operations Research, or a related field.
• Minimum of 4 years of related work experience (graduate research included).
• Experience developing models in one or more of the following: steady-state simulation, dynamic simulation, computational fluid dynamics (CFD), discrete event simulation, nonlinear programming, or mixed-integer linear programming.

Additional Skills/Preferences

• Strong technical writing and presentation skills.
• Capability to solve complex issues with minimal supervision.
• Demonstrated working knowledge of the underlying physics, first-principles concepts, and numerical methods from which computational models are formulated and solved.
• Experience with chemical process modeling, scale-up, and unit operation simulation.
• Experience with capacity analysis, line balancing, scheduling, or logistics optimization in a manufacturing environment.
• Experience developing simulation and optimization models leveraging commercial software tools such as ANSYS-Fluent®, M-Star®, COMSOL®, aspenONE®, AFT Fathom®, ExtendSim®, or Frontline Solver®.
• Experience with statistical analysis and data/model fitting leveraging commercial software tools such as JMP® or similar.
• Experience developing digital twins of manufacturing processes or production lines.
• Experience working with validated systems and in a GMP/pharmaceutical industry environment.
• Ability to work well across different cultures and global manufacturing sites.