Industrial Manufacturing Engineer

Tekskills Inc.; Pittsburgh (Allegheny County); On-site; USD 90,000 - 130,000; Full time

Job summary

Responsibilities include building scalable IE models, performing scenario analyses, and collaborating with cross-functional teams to optimize layout, throughput, and cost. Strong Excel, PFEP knowledge, and Six Sigma are required.

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.

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 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 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 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 to align models with real-world constraints and business needs.
• Translate analytical outputs into executive-level insights and recommendations.

Skills

Capacity Planning

Industrial Manufacturing

Material Planning

CAPEX management

PFMEA

Lean Manufacturing

Layout planning

Six Sigma

Simulation tools

Excel

AI-driven tools

PFEP

OEE modeling

Power BI

Python

SQL

Education

Bachelor's degree in industrial engineering

Tools

FlexSim

AnyLogic

Simio

Power BI

Python

SQL

Job description

Job Title: Industrial Manufacturing Engineer

Location: Pittsburgh, PA (Onsite)

Duration: 6-12 Months

Skills

• Capacity Planning
• Industrial Manufacturing
• Material Planning
• Greenfield or brownfield project experience
• Equipment planning
• Labour planning
• CAPEX management
• PFMEA
• Lean Manufacturing
• Layout planning
• Knowledge of AI-driven tool

Required Skill

• Greenfield or brownfield project experience
• Capacity planning
• Labour planning
• CAPEX management
• Capital investments – ROI, IRR, NPV, and cost-benefit analysis
• Design and maintain OEE model
• Support factory ramp-up, installation, and operational readiness through model validation and performance tracking
• Material planning
• PFEP
• Lean Manufacturing
• Six Sigma
• Layout planning
• Simulation tools experience
• Strong expertise in Excel
• Knowledge of AI-driven tools

Job Description

• 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.

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 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 analysis
• Develop standardized systems, frameworks, and governance for data modeling, analytics, and reporting
• Automate data collection, validation, and reporting pipelines using AI and advanced analytics
• 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

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)
• Familiarity with AI/ML applications in manufacturing analytics
• 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