Senior Industrial Analytics Engineer

Amazon is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon"s scale. We"re building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what"s possible in robotic manipulation, locomotion, and human-robot interaction.

The Senior 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 job 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