QA Optimization Engineer

The Quality Optimization Engineer is responsible for identifying, analyzing, and eliminating manufacturing yield loss while ensuring operational capabilities align precisely with product specifications. Operating with a high degree of autonomy, this individual acts as a critical liaison between Operations, Quality, R&D, and Engineering to clarify quality standards and reduce operational ambiguity.  The ideal candidate possesses exceptional critical thinking skills, a strong analytical mindset, and a proactive approach to cross-functional leadership. They leverage foundational engineering principles alongside modern technologies—including AI-driven data tools and predictive analytics—to drive sustainable yield improvements and optimize manufacturing processes.  Primary Responsibilities • Yield & Loss Optimization: Lead investigative efforts to identify drivers of manufacturing loss, scrap, and downtime; design and execute corrective strategies to improve overall equipment effectiveness (OEE). • Autonomous Problem Solving: Drive root-cause investigations from end-to-end utilizing formal methodologies (e.g., 8D, DMAIC, 5-Why, Fishbone Analysis) with minimal supervisory oversight. • Standard Clarification & Operational Alignment: Partner directly with Operations leadership to clarify quality standards, establish unambiguous visual/measurable criteria, and ensure floor-level understanding to prevent operational losses.  • Specification & Capability Alignment: Assist in drafting, refining, and reviewing product specifications; evaluate current manufacturing process capabilities (Cpk/Ppk) to ensure new and existing specs are technically achievable on the production line.  • Cross-Functional Collaboration: Partner seamlessly across Production, Engineering, Maintenance, and R&D to implement sustainable process controls without relying on top-down authority.  • Leverage Advanced & AI Tooling: Integrate artificial intelligence tools, machine learning applications, and modern data platforms to enhance data synthesis, predictive modeling, and root-cause discovery. • Process Standardization: Translate problem-solving outcomes and specification updates into standard operating procedures (SOPs), control plans, and training materials to safeguard against defect recurrence.  • Metrics & Reporting: Generate and present quantitative performance metrics, capability studies, and project updates to plant and operations management.  • Quality Testing & Validation: Conduct targeted age testing on key product lines to validate long-term performance and reliability, integrating findings directly into problem-solving efforts and quality investigations. • Analytical Mindset: Exceptional critical thinking skills with a demonstrated ability to evaluate complex system inputs, isolate variables, and synthesize actionable solutions. • Self-Direction: Proven ability to manage multiple priorities independently, establish project scope, and drive alignment across multi-disciplinary teams. • Operational Integration: Ability to bridge technical specifications with practical manufacturing realities, translating engineering tolerances into actionable operational standards.  • Technological Capability: Proficiency in leveraging AI productivity and analytics tools (e.g., predictive analytics models, LLM-assisted data processing) alongside traditional statistical analysis software (Minitab). • Communication: Superior verbal and written communication skills, capable of translating technical analysis into clear, strategic recommendations for internal stakeholders. Education & Experience Requirements • Education: Bachelor's degree in Engineering  • Experience: 1+ years of engineering experience within a manufacturing environment. • Preferred Experience: Expos