Advisor - Computational Modeling Engineer

Eli Lilly Eli Lilly · Pharma · Indianapolis, IN

The Advisor - Computational Modeling Engineer 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. A core focus of the role is the development of Digital Twin computational models that provide online modeling capabilities to manufacturing facilities.

What you'd actually do

  1. Develop and deploy simulation and optimization models to drive decisions that help design new and/or improve existing manufacturing and logistics processes.
  2. Apply a broad range of modeling techniques as appropriate to the problem — including steady-state and dynamic process simulation, computational fluid dynamics (CFD) and multiphysics simulation, discrete event simulation, and nonlinear/mixed-integer optimization — across both continuous/batch and discrete manufacturing domains.
  3. Develop Digital Twin computational models to provide online modeling capabilities to Lilly manufacturing facilities.
  4. Deliver sound interpretation of modeling results by maintaining in-depth knowledge of the underlying physics, first-principles concepts, and numerical methods from which models are formulated and solved.
  5. Complement model-based solutions with laboratory investigation, empirical validation, or validation against site operating data when appropriate.

Skills

Required

  • MS degree in Chemical Engineering, Mechanical Engineering, Industrial Engineering, Operations Research, or a related field
  • Deep technical expertise in computational modeling and simulation
  • Strong fundamentals in the underlying physics, mathematics, and first-principles concepts
  • Ability to complement model-based solutions with laboratory investigation, empirical validation, or validation against site operating data

Nice to have

  • Computational Fluid Dynamics (CFD)
  • Multiphysics simulation
  • Discrete event simulation
  • Nonlinear/mixed-integer optimization
  • Digital Twin development