QA Optimization Engineer

Job description

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.

Qualifications

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

Exposure to process capability analysis (SPC/Cpk), spec-setting workflows, Lean Six Sigma concepts, or formal Root Cause Analysis (RCA).