What are the responsibilities and job description for the Industrial Engineering position at Bellatrix Systems LLC?
Job Title: Industrial Engineering Analytics Engineer (Manufacturing Systems & Modeling)
Location: Pittsburgh, PA (Onsite)
Positions: 2
Required Skills
The Industrial Engineering Analytics Engineer will lead the development and application of advanced analytical models to drive manufacturing efficiency, capacity planning, and cost
optimization.
This role is responsible for building and managing integrated IE models that connect capacity, labor, material flow, PFEP, and cost (COGS) to enable data-driven decisionmaking across factory and site operations.
The ideal candidate will combine strong industrial engineering fundamentals with advanced analytics, simulation, business case development, and AI-driven systems to support large-scale manufacturing environments.
Role Overview
The Industrial Engineering Analytics Engineer will lead the development and application of advanced analytical models to drive manufacturing efficiency, capacity planning, and cost optimization.
This role is responsible for building and managing integrated IE models that connect capacity, labor, material flow, PFEP, and cost (COGS) to enable data-driven decision making across factory and site operations. The ideal candidate will combine strong industrial engineering fundamentals with advanced analytics, simulation, business case development, and AI-driven systems to support large-scale manufacturing environments.
Key Responsibilities
Develop and own integrated IE models that connect capacity, labor, material flow, PFEP, and cost (COGS) to support factory planning and operations
Build and maintain capacity models (target vs. forecast vs. gated capacity), incorporating cycle time, OEE, yield losses, and bottleneck analysis
Develop labor models to optimize headcount, utilization, and labor cost (LOH) across production systems
Create and evaluate business cases for capital investments, including ROI, IRR, NPV, and cost benefit analysis
Lead COGS modeling, including labor, overhead, scrap, and process-driven cost components
Develop and track scrap and yield models, quantifying cost impact and identifying improvement opportunities
Design and maintain OEE models (availability, performance, quality) to drive operational efficiency and continuous improvement
Perform buffer and WIP analysis to optimize inline and interline storage, reduce bottlenecks, and stabilize production flow
Develop process flow diagrams (PFDs) and value stream maps to represent manufacturing systems and identify inefficiencies
Integrate PFEP (Plan for Every Part) data into models to optimize material flow, storage, and line-side delivery strategies
Support factory layout, site planning, and material flow decisions through data-driven insights and modeling
Perform scenario analysis and sensitivity studies to evaluate production strategies and capacity expansion plans
Utilize and/or develop factory simulation models (e.g., FlexSim, AnyLogic, Simio) to analyze throughput, bottlenecks, and system performance
Support factory ramp-up, installation, and operational readiness through model validation and performance tracking
Collaborate with cross-functional teams (Manufacturing, Operations, Supply Chain, Finance,
Engineering) to align models with real-world constraints and business needs
Translate complex analytical outputs into clear, executive-level insights and recommendations
Collaborate with MES and Controls teams to integrate shop-floor data with IE models, ensuring accurate OEE measurement and enabling real-time, scalable dashboards for operational visibility and executive decision-making
AI & Data Systems
Introduce and implement AI-driven tools and platforms to enhance industrial engineering analytics and decision-making
Design and manage scalable data models and data architecture for IE, capacity, labor, PFEP,and cost analytics
Develop standardized systems, frameworks, and governance for data modeling, analytics, and reporting
Automate data collection, validation, and reporting pipelines using AI and advanced analytics tools
Enable predictive analytics and intelligent decision-making for capacity, throughput, and cost optimization
Establish best practices for data quality, model standardization, and system integration across the organization
Basic 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, or operations analysis
Strong understanding of manufacturing systems, capacity planning, and industrial engineering principles
Preferred Qualifications
Experience building end-to-end IE models integrating capacity, labor, cost, PFEP, and material flow
Proficiency in capacity modeling, OEE analysis, cycle time studies, and line balancing
Hands-on experience with PFEP, material flow optimization, and warehouse integration
Experience with factory simulation tools (e.g., FlexSim, AnyLogic, Simio)
Strong experience in business case development (ROI, IRR, NPV)
Knowledge of COGS modeling, cost structures, and financial impact analysis
Experience with data analysis tools (Excel advanced modeling, Python, SQL, Power BI/Tableau, or similar)
Familiarity with AI/ML applications in manufacturing analytics (preferred)
Familiarity with lean manufacturing and continuous improvement methodologies
Key Skills & Competencies
Strong analytical and problem-solving skills with a data-driven mindset
Ability to build scalable models and analytics systems that support both tactical and strategic decisions
Strong communication skills to translate complex data into actionable insights
Ability to work across cross-functional teams and influence decision-making
Attention to detail with a systems-level understanding of manufacturing operations
Ability to manage multiple projects and priorities in a fast-paced environment
Location: Pittsburgh, PA (Onsite)
Positions: 2
Required Skills
- Greenfield or brownfield project experience (good to have)
- Equipment planning
- Capacity planning
- Labour planning
- CAPEX management (good to have)
- Supplier validation
- Capital investments ROI, IRR, NPV, and cost-benefit analysis
- Design and maintain OEE models
- Support factory ramp-up, installation, and operational readiness through model validation and performance tracking
- Material planning
- PFMEA
- Lean Manufacturing
- Six Sigma
- Layout planning (good to have)
- Simulation tools experience (not mandatory)
- Strong expertise in Excel
- Knowledge of AI-driven tools (good to have)
The Industrial Engineering Analytics Engineer will lead the development and application of advanced analytical models to drive manufacturing efficiency, capacity planning, and cost
optimization.
This role is responsible for building and managing integrated IE models that connect capacity, labor, material flow, PFEP, and cost (COGS) to enable data-driven decisionmaking across factory and site operations.
The ideal candidate will combine strong industrial engineering fundamentals with advanced analytics, simulation, business case development, and AI-driven systems to support large-scale manufacturing environments.
Role Overview
The Industrial Engineering Analytics Engineer will lead the development and application of advanced analytical models to drive manufacturing efficiency, capacity planning, and cost optimization.
This role is responsible for building and managing integrated IE models that connect capacity, labor, material flow, PFEP, and cost (COGS) to enable data-driven decision making across factory and site operations. The ideal candidate will combine strong industrial engineering fundamentals with advanced analytics, simulation, business case development, and AI-driven systems to support large-scale manufacturing environments.
Key Responsibilities
Develop and own integrated IE models that connect capacity, labor, material flow, PFEP, and cost (COGS) to support factory planning and operations
Build and maintain capacity models (target vs. forecast vs. gated capacity), incorporating cycle time, OEE, yield losses, and bottleneck analysis
Develop labor models to optimize headcount, utilization, and labor cost (LOH) across production systems
Create and evaluate business cases for capital investments, including ROI, IRR, NPV, and cost benefit analysis
Lead COGS modeling, including labor, overhead, scrap, and process-driven cost components
Develop and track scrap and yield models, quantifying cost impact and identifying improvement opportunities
Design and maintain OEE models (availability, performance, quality) to drive operational efficiency and continuous improvement
Perform buffer and WIP analysis to optimize inline and interline storage, reduce bottlenecks, and stabilize production flow
Develop process flow diagrams (PFDs) and value stream maps to represent manufacturing systems and identify inefficiencies
Integrate PFEP (Plan for Every Part) data into models to optimize material flow, storage, and line-side delivery strategies
Support factory layout, site planning, and material flow decisions through data-driven insights and modeling
Perform scenario analysis and sensitivity studies to evaluate production strategies and capacity expansion plans
Utilize and/or develop factory simulation models (e.g., FlexSim, AnyLogic, Simio) to analyze throughput, bottlenecks, and system performance
Support factory ramp-up, installation, and operational readiness through model validation and performance tracking
Collaborate with cross-functional teams (Manufacturing, Operations, Supply Chain, Finance,
Engineering) to align models with real-world constraints and business needs
Translate complex analytical outputs into clear, executive-level insights and recommendations
Collaborate with MES and Controls teams to integrate shop-floor data with IE models, ensuring accurate OEE measurement and enabling real-time, scalable dashboards for operational visibility and executive decision-making
AI & Data Systems
Introduce and implement AI-driven tools and platforms to enhance industrial engineering analytics and decision-making
Design and manage scalable data models and data architecture for IE, capacity, labor, PFEP,and cost analytics
Develop standardized systems, frameworks, and governance for data modeling, analytics, and reporting
Automate data collection, validation, and reporting pipelines using AI and advanced analytics tools
Enable predictive analytics and intelligent decision-making for capacity, throughput, and cost optimization
Establish best practices for data quality, model standardization, and system integration across the organization
Basic 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, or operations analysis
Strong understanding of manufacturing systems, capacity planning, and industrial engineering principles
Preferred Qualifications
Experience building end-to-end IE models integrating capacity, labor, cost, PFEP, and material flow
Proficiency in capacity modeling, OEE analysis, cycle time studies, and line balancing
Hands-on experience with PFEP, material flow optimization, and warehouse integration
Experience with factory simulation tools (e.g., FlexSim, AnyLogic, Simio)
Strong experience in business case development (ROI, IRR, NPV)
Knowledge of COGS modeling, cost structures, and financial impact analysis
Experience with data analysis tools (Excel advanced modeling, Python, SQL, Power BI/Tableau, or similar)
Familiarity with AI/ML applications in manufacturing analytics (preferred)
Familiarity with lean manufacturing and continuous improvement methodologies
Key Skills & Competencies
Strong analytical and problem-solving skills with a data-driven mindset
Ability to build scalable models and analytics systems that support both tactical and strategic decisions
Strong communication skills to translate complex data into actionable insights
Ability to work across cross-functional teams and influence decision-making
Attention to detail with a systems-level understanding of manufacturing operations
Ability to manage multiple projects and priorities in a fast-paced environment