Industrial Engineer/Analytics Engineer (Manufacturing Systems & Modeling)
As an Industrial Engineering Analytics Engineer you will be responsible for;
Developing and managing integrated industrial engineering models that connect capacity, labor, material flow, PFEP, and cost to support factory planning and operations.
Building and maintaining capacity, labor, OEE, yield, scrap, and COGS models to drive operational efficiency and decision-making.
Performing bottleneck analysis, scenario planning, sensitivity studies, and capacity expansion evaluations.
Creating business cases for capital investments, including ROI, IRR, NPV, and cost-benefit analysis.
Supporting factory layout, site planning, material flow optimization, and production strategy decisions through data-driven insights.
Developing and utilizing factory simulation models to analyze throughput, system performance, and operational readiness.
Collaborating with Manufacturing, Operations, Supply Chain, Finance, Engineering, MES, and Controls teams to align models with business requirements.
Designing scalable data models, reporting frameworks, dashboards, and analytics systems to improve operational visibility.
Implementing AI-driven tools, predictive analytics, and automated reporting solutions to enhance manufacturing performance.
Presenting complex analytical findings and recommendations to leadership and executive stakeholders.
Qualifications;
Bachelor’s degree in Industrial Engineering, Mechanical Engineering, Operations Research, or a related field.
7+ years of experience in industrial engineering analytics, manufacturing modeling, operations analysis, or a related discipline.
Strong experience building capacity and labor models from the ground up.
Expertise with OEE modeling, including availability, performance, and quality metrics.
Advanced proficiency with Excel, Python, SQL, and reporting tools such as Power BI or Tableau.
Experience developing business cases using ROI, IRR, NPV, and cost-benefit analysis methodologies.
Knowledge of COGS modeling, manufacturing cost structures, yield analysis, and scrap reduction strategies.
Experience with factory simulation tools such as FlexSim, AnyLogic, Simio, or similar platforms.
Strong understanding of manufacturing systems, capacity planning, material flow, and industrial engineering principles.
Excellent analytical, problem-solving, and executive communication skills.
Experience translating complex data into actionable recommendations for technical and non-technical stakeholders.
Familiarity with AI-driven analytics, predictive modeling, and continuous improvement methodologies is preferred.
Salary Range: $90K-$135K
The actual salary offered is dependent on various factors including, but not limited to, location, the candidate’s combination of job-related knowledge, qualifications, skills, education, training, and experience.