Analytics Engineer Jobs

About The Position

Requirements

• 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.
• Robotics experience is listed as a mandatory skill.
• The posting separately lists 5–8 years of experience.

Responsibilities

• Build and maintain capacity models comparing target, forecast, and 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 capital investment business cases, including ROI, IRR, NPV, and cost-benefit analysis.
• Lead COGS modeling for labor, overhead, scrap, and process-driven cost components.
• Support factory layout, site planning, and material flow decisions using data-driven insights and modeling.
• Conduct scenario analysis and sensitivity studies to evaluate production strategies and capacity expansion plans.
• Use 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 Manufacturing, Operations, Supply Chain, Finance, and Engineering teams to align models with real-world constraints and business needs.
• Translate analytical outputs into executive-level insights and recommendations.
• Collaborate with MES and Controls teams to integrate shop-floor data with industrial engineering models, improve OEE measurement, and enable scalable dashboards.
• Introduce and implement AI-driven tools and platforms for industrial engineering analytics and decision-making.
• Design and manage scalable data models and data architecture for industrial engineering, capacity, labor, 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.

Benefits

• Medical and dental benefits options
• Disability insurance
• Paid time off, inclusive of sick leave
• Other paid and unpaid leave options