Job Description

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

Computational Modeling Engineer – Discrete Manufacturing Modeling

About Lilly

Manufacturing & Quality Technical Hub, Hyderabad

Digital Manufacturing Engineering (DME) is part of the Corporate Engineering Tech Center (ETC). The ETC is a technical resource for training, standards, project implementation, and the center of engineering excellence for the corporation, providing deeply technical resources to solve the most intractable technical problems in manufacturing. DME provides Digital Engineering Platform ownership, OT data integration solutions, engineering data standardization/ownership, and digital manufacturing solutions for Lilly Manufacturing. Our team focuses on connecting our facilities (securely) and leveraging the data from those facilities to deliver digital manufacturing solutions to improve manufacturing productivity, data integrity / compliance, quality, and operational excellence. Digital Manufacturing Technologies include digital twins, computational modeling, edge/IoT, and analytics solutions. Digital Engineering Platforms include CMMS (Computerized Maintenance Management System), GECMS (Global Engineering Content Management System) and many other digital platforms leveraged by engineers across Lilly Manufacturing.

and Responsibilities

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.

Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form ( https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.

Lilly does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status.

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