Lead Engineer, Digital Twin Systems, PET

J.M. Smucker Co•Topeka, KS

4d•Onsite

About The Position

Lead the overall Digital Twin initiative for Pet. The initial phase will focus on the Topeka operation and evolve with reapplication to the other PET plants. You will be responsible for designing, developing, and scaling digital twins of Smucker manufacturing assets, processes, and systems to drive measurable improvements in safety, quality, throughput, uptime, cost, and sustainability. This role bridges operations, data, and analytics by translating real‑world manufacturing behavior into virtual models that enable monitoring, simulation, prediction, and optimization.

Requirements

Bachelor’s degree in Engineering, Data Science, or related field required
Minimum of 5-years’ experience in manufacturing, process engineering, operations, or industrial analytics.
Strong understanding of manufacturing processes, equipment behavior, and operational KPIs.
Experience working with time‑series data, process data, and industrial data sources.

Nice To Haves

Hands‑on experience with digital twin platforms, advanced analytics tools, or ML frameworks.
Experience with predictive modeling, regression, clustering, anomaly detection, or optimization techniques.
Familiarity with manufacturing execution systems (MES), historians, PLC/SCADA data, or IIoT architectures.
Ability to communicate complex analytical insights in a clear, actionable way to non‑technical audiences.

Responsibilities

Lead cross-functional team including Operations, Cost of Quality technology workstream, Technical Services, and Engineering to design and maintain digital twins representing physical assets, production lines, and end‑to‑end manufacturing processes.
Organization of strategy, execution of plan, and communication to leadership.
Develop in-house Digital Twin expertise within PET SBA.
Integrate historical and real‑time data (sensor, process, quality, and setpoint data) into digital twin models.
Apply statistical modeling, machine learning, and advanced analytics to enable: Predictive maintenance and failure forecasting, Anomaly detection and fault identification, Waste, downtime, and energy optimization.
Partner with Operations, Engineering, Quality, R&D, and OpEx teams to identify high‑value digital twin use cases.
Translate digital twin insights into actionable recommendations, controls, and operating standards.
Support pilot deployment, scale‑up, and replication of successful digital twin use cases across sites.
Optimize process parameters to improve CpK, yield, throughput, and asset reliability.
Identify data quality gaps, sensor drift, missing data, and inconsistencies.
Collaborate with IT/IS and controls teams to improve data pipelines, tagging standards, and contextualization.
Act as a technical and operational translator between plant teams and data/analytics partners.
Support training and change management to embed digital twin insights into daily operations.
Contribute to digital manufacturing standards, best practices, and governance frameworks.