Computational Modeling Engineer

Job Summary

The Computational Modeling Engineer - Discrete Manufacturing Modeling will provide deep modeling expertise and develop computational models that drive decisions to design new manufacturing systems and improve existing ones across Lilly's global manufacturing network. This role focuses on the dynamics of discrete manufacturing operations - parenteral fill/finish, device assembly, packaging, and material handling - applying modeling techniques across discrete event simulation, capacity and throughput analysis, line balancing, scheduling, and resource/operational logistics optimization. A core focus of the role is the development of Digital Twin computational models - including discrete event and optimization-based representations - 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.

The primary focus of the role is to develop and deploy discrete event simulation and optimization models that increase manufacturing capacity, resilience, compliance, and inspection readiness, and that support operational excellence goals for safety, quality, and supply. The engineer will also serve as an internal advocate for simulation-based and model-based methods, fostering the use of discrete modeling tools by colleagues outside DME through instruction, consultation, and the development of internal collaborations.

Key Objectives / Deliverables

• Develop and deploy discrete event simulation and optimization models to drive decisions that help design new and/or improve existing manufacturing and logistics processes.
• Develop capacity, throughput, line-balancing, and bottleneck-analysis models for parenteral fill/finish, device assembly, packaging, and material handling operations.
• Develop Digital Twin computational models of discrete manufacturing lines and logistics networks to provide online modeling capabilities to Lilly manufacturing facilities.
• Deliver sound interpretation of modeling results by maintaining in-depth knowledge of the underlying first-principles concepts (queueing theory, stochastic processes, scheduling theory) and numerical methods from which discrete event and optimization models are formulated and solved.
• Complement model-based solutions with empirical validation against site operating data and, when appropriate, time studies or shop-floor investigation.
• Prepare internal technical reports to document work and to contribute to the product or process body of knowledge, and when appropriate, publish and/or present work externally.
• Maintain and grow the functional value of discrete manufacturing modeling within Lilly by developing appropriate internal collaborations and sharing key learnings broadly at appropriate internal forums so colleagues can identify projects that may benefit from model-based analysis.
• Foster the use of discrete simulation and optimization tools by colleagues outside DME by providing instruction, consultation, and continuously advocating the use of simulation-based and model-based methods.
• Identify and implement modeling advances that bring value to Lilly by maintaining awareness of new developments in the external discrete event simulation and optimization landscape.
• Develop collaborations with appropriate external partners to help manage workload and to gain additional expertise.
• Partner with data leads within DME to provide data requirements that support discrete manufacturing models and Digital Twin solutions.

Basic Qualifications

• PhD with 4-8 years of experience or MS degree with 8-15 years of experience in Industrial Engineering, Operations Research, Chemical Engineering, Mechanical Engineering, 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: discrete event simulation, nonlinear programming, mixed-integer linear programming, or dynamic simulation of discrete systems.
• Demonstrated working knowledge of queueing theory, stochastic processes, and the numerical/optimization methods from which discrete event and optimization models are formulated and solved.

Additional Skills / Preferences

• Strong technical writing and presentation skills.
• Capability to solve complex issues with minimal supervision.
• Experience with capacity analysis, line balancing, scheduling, and logistics optimization in a manufacturing environment.
• Experience developing simulation and optimization models leveraging commercial software tools such as ExtendSim, Frontline Solver, aspenONE, or equivalent discrete event simulation platforms.
• Experience with statistical analysis and data/model fitting leveraging commercial software tools such as JMP or similar.
• Experience developing digital twins of discrete manufacturing lines (e.g., parenteral fill/finish, device assembly, packaging).
• Experience working with validated systems and in a GMP/pharmaceutical industry environment.
• Ability to work well across different cultures and global manufacturing sites.

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