Industrial Engineering Analytics Engineer

ZENITH INFOTEK LLC•Pittsburgh, PA

11d•$50 - $70

About The Position

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.

Requirements

Bachelor’s degree in Industrial Engineering, Mechanical Engineering, Operations Research, or a related technical field.
7+ years of experience in industrial engineering analytics, manufacturing modeling, operations analysis, or a related field.
Strong understanding of manufacturing systems, capacity planning, production systems, and industrial engineering principles.
Demonstrated experience developing data-driven models and analytical solutions for manufacturing or factory operations.
Strong analytical, quantitative, and problem-solving skills with a data-driven mindset.
Ability to build scalable models and analytics systems supporting both tactical and strategic manufacturing decisions.
Strong understanding of manufacturing operations and systems-level thinking.
Ability to translate complex analytical results into clear, actionable business recommendations.
Strong communication and presentation skills, including the ability to communicate with executive leadership.
Proven ability to collaborate effectively with Manufacturing, Operations, Supply Chain, Finance, Engineering, MES, and Controls teams.
Strong project management skills with the ability to manage multiple projects and priorities in a fast-paced environment.
High attention to detail combined with a strong understanding of end-to-end manufacturing processes.
Ability to influence cross-functional stakeholders and drive data-based decision-making.
Strong understanding of data quality, analytics governance, model standardization, and scalable system design.

Nice To Haves

Greenfield or brownfield project experience
CAPEX management
Layout planning
Simulation tools experience (not mandatory)
Knowledge of AI-driven tools
Experience building end-to-end IE models integrating capacity, labor, cost, PFEP, material flow, and operational performance.
Strong proficiency in capacity modeling, OEE analysis, cycle-time studies, line balancing, bottleneck analysis, and production optimization.
Hands-on experience with PFEP, material flow optimization, warehouse integration, and line-side delivery.
Experience with factory simulation tools such as FlexSim, AnyLogic, Simio, or equivalent.
Strong experience developing business cases using ROI, IRR, NPV, payback, and cost-benefit analysis.
Knowledge of COGS modeling, manufacturing cost structures, labor costs, overhead, scrap, yield, and financial impact analysis.
Experience with data analysis and visualization tools such as Advanced Excel, Python, SQL, Power BI, Tableau, or similar technologies.
Familiarity with AI/ML applications in manufacturing analytics, predictive analytics, and intelligent decision-support systems.
Experience with MES, shop-floor data, controls systems, manufacturing data integration, and real-time operational dashboards.
Familiarity with Lean Manufacturing, Six Sigma, Value Stream Mapping, Kaizen, and continuous improvement methodologies.

Responsibilities

Develop and own integrated Industrial Engineering (IE) models connecting capacity, labor, material flow, PFEP, and COGS to support factory planning and operations.
Build and maintain capacity models covering target, forecast, and gated capacity, incorporating cycle time, OEE, yield losses, utilization, and bottleneck analysis.
Develop labor models to optimize headcount, workforce utilization, labor productivity, and labor cost/LOH across production systems.
Lead COGS modeling, including labor, overhead, scrap, and process-driven cost components.
Develop and track scrap and yield models, quantify financial impacts, and identify opportunities for operational improvement.
Design and maintain OEE models covering availability, performance, and 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 (VSMs) to represent manufacturing systems and identify process inefficiencies.
Integrate PFEP (Plan for Every Part) data into IE models to optimize material flow, storage, inventory, and line-side delivery strategies.
Support factory layout, site planning, equipment placement, and material flow decisions through data-driven modeling and analysis.
Conduct scenario analysis and sensitivity studies to evaluate production strategies, capacity expansion plans, and operational trade-offs.
Create and evaluate business cases for capital investments and manufacturing improvements.
Perform ROI, IRR, NPV, payback period, and cost-benefit analysis to support investment decisions.
Quantify the financial impact of capacity constraints, yield losses, scrap, labor requirements, and process improvements.
Partner with Finance and Operations teams to align operational models with financial targets and business objectives.
Develop and/or utilize factory simulation models using tools such as FlexSim, AnyLogic, Simio, or similar platforms.
Analyze throughput, bottlenecks, WIP, resource utilization, cycle times, and overall system performance.
Support factory ramp-up, equipment installation, production launch, and operational readiness through model validation and performance tracking.
Validate IE models against real-world manufacturing constraints, production data, and shop-floor performance.
Collaborate with MES and Controls teams to integrate shop-floor data with IE models and analytics platforms.
Ensure accurate and consistent OEE measurement through reliable integration of production and equipment data.
Enable real-time and scalable dashboards that provide operational visibility to manufacturing teams and executive leadership.
Translate complex analytical outputs into clear, actionable, and executive-level insights and recommendations.
Introduce and implement AI-driven tools, platforms, and advanced analytics to enhance industrial engineering analysis and decision-making.
Design and manage scalable data models and data architectures supporting IE, capacity, labor, PFEP, OEE, and cost analytics.
Develop standardized frameworks, systems, processes, and governance for data modeling, analytics, and reporting.
Automate data collection, validation, transformation, analysis, and reporting pipelines using AI, automation, and advanced analytics technologies.
Enable predictive analytics and intelligent decision-making for capacity, throughput, labor, bottleneck, and cost optimization.
Establish best practices for data quality, model standardization, data governance, and system integration across manufacturing and operations.
Identify opportunities to leverage AI/ML, predictive modeling, and intelligent automation to improve factory performance and operational decision-making.