Industrial Engineer/Analytics Engineer (Manufacturing Systems & Modeling)

ALTEN Technology USA•North Reading, MA

7d•$90,000 - $135,000

About The Position

ALTEN Technology USA is seeking an Industrial Engineering Analytics Engineer to develop and manage integrated industrial engineering models that connect capacity, labor, material flow, PFEP, and cost for factory planning and operations. The role builds and maintains models for capacity, labor, OEE, yield, scrap, and COGS to support operational efficiency and decision-making. The engineer evaluates bottlenecks, scenarios, sensitivities, and capacity expansions; develops capital-investment business cases; and supports factory layout, site planning, material-flow optimization, and production strategy with data-driven insights. The role also uses factory simulation to analyze throughput, system performance, and operational readiness. Working across Manufacturing, Operations, Supply Chain, Finance, Engineering, MES, and Controls, the engineer aligns models with business needs and creates scalable data models, reporting frameworks, dashboards, and analytics systems. The role also applies AI-driven tools, predictive analytics, and automated reporting to improve manufacturing performance, and presents findings and recommendations to leadership and executive stakeholders. ALTEN Technology USA is an engineering company working across industries including aerospace, medical devices, robotics, automotive, commercial vehicles, EVs, and rail. The company is part of the ALTEN Group. The position requires successful completion of a pre-employment drug screening as a condition of hire.

Requirements

Bachelor’s degree in Industrial Engineering, Mechanical Engineering, Operations Research, or a related field.
At least 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 in 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.
Successful completion of a pre-employment drug screening is required as a condition of hire.

Nice To Haves

Familiarity with AI-driven analytics, predictive modeling, and continuous improvement methodologies.

Responsibilities

Develop and manage integrated industrial engineering models connecting capacity, labor, material flow, PFEP, and cost.
Build and maintain capacity, labor, OEE, yield, scrap, and COGS models.
Perform bottleneck analysis, scenario planning, sensitivity studies, and capacity expansion evaluations.
Create capital-investment business cases using ROI, IRR, NPV, and cost-benefit analysis.
Support factory layout, site planning, material-flow optimization, and production strategy decisions through data-driven insights.
Develop and use factory simulation models to analyze throughput, system performance, and operational readiness.
Collaborate with Manufacturing, Operations, Supply Chain, Finance, Engineering, MES, and Controls teams to align models with business requirements.
Design scalable data models, reporting frameworks, dashboards, and analytics systems to improve operational visibility.
Implement AI-driven tools, predictive analytics, and automated reporting solutions to improve manufacturing performance.
Present analytical findings and recommendations to leadership and executive stakeholders.