Process Modeling Engineer

Lubrizol Corporation•Wickliffe, OH

37d•Onsite

About The Position

The Process Modeling Engineer plays a critical role in optimizing and transforming manufacturing performance through advanced process modeling, simulation, and data-driven analysis. This role develops and deploys both physics-based and data-based models to support process design, scale-up, troubleshooting, optimization, and long-term strategic planning. The engineer also leads the creation of modeling standards, training programs, and strategic frameworks that elevate modeling capabilities across the organization. This role is an essential driver of manufacturing excellence—helping the organization improve productivity, reduce variability, enhance safety, and accelerate innovation. By advancing both physics- and data-based modeling capabilities, the Process Modeling Engineer plays a key part in the company’s digital transformation and long-term competitiveness.

Requirements

Master’s or PhD in Chemical Engineering, Process Modeling, Computational Methods, or related fields.
3+ years of experience in process engineering, process modeling, or advanced analytics within chemical, materials, or related manufacturing industries.
Strong experience with process simulation tools (e.g., Aspen Plus, Aspen HYSYS, CHEMCAD, gPROMS)
Experience developing data-driven models using tools like Python, MATLAB, JMP, or machine learning platforms.
Strong foundation in transport phenomena, thermodynamics, kinetics, and unit operations.
Proficiency in data analysis, statistics, and visualization.
Ability to integrate lab, pilot, and plant data into robust model frameworks.
Strong communication skills with ability to explain complex modeling results clearly.
Demonstrated leadership in influencing without authority and driving standards or programs across teams.
Structured problem-solving, curiosity, and continuous improvement mindset.

Nice To Haves

Experience with dynamic modeling, digital twins, or real-time optimization is a plus.

Responsibilities

Develop, validate, and maintain physics-based process models (e.g., heat/mass transfer, reaction kinetics, fluid dynamics, unit operations).
Build and apply data-driven models using statistical, machine learning, or hybrid modeling approaches to identify trends, correlations, and optimization opportunities.
Conduct steady-state and dynamic simulations for process design, capacity analysis, debottlenecking, and energy efficiency assessments.
Support troubleshooting and root cause analysis by using modeling to reproduce plant behavior and test hypotheses.
Collaborate with R&D, operations, and process engineering to translate lab/pilot data into full-scale manufacturing models.
Develop and maintain modeling standards, documentation practices, and validation protocols to ensure consistency, quality, and sustainability of modeling assets.
Establish templates, guidelines, and workflows for physics-based and data-driven modeling across the engineering organization.
Lead governance activities to ensure models are used responsibly, accurately, and with defined lifecycle management.
Design and deliver training programs, workshops, and hands-on sessions for engineers, operators, and technical staff on modeling tools, methodologies, and best practices.
Mentor engineering teams on model interpretation, limitations, and appropriate use in decision-making.
Help cultivate a culture of data literacy, model-informed design, and digital fluency across the organization.
Develop and communicate a strategic roadmap for modeling capabilities, including tool selection, digital technologies, and long-term capability growth.
Evaluate and integrate emerging modeling technologies—such as AI/ML, advanced simulation platforms, and digital twin solutions.
Collaborate with global engineering and operations leaders to align modeling strategy with broader engineering, manufacturing, and corporate objectives.
Provide thought leadership in the areas of advanced analytics, digital process design, and predictive manufacturing.
Work closely with manufacturing, quality, R&D, EHS, and supply chain teams to ensure models support operational excellence, process safety, and product quality.
Support capital project teams with modeling contributions for feasibility studies, conceptual design, and detailed engineering.
Present model results and recommendations to technical and non-technical audiences, including senior leadership.