QA Optimization Engineer

Avery Dennison

Painesville, OHOn-siteFull time
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Pay
$75K to $100K
a year
Location
Painesville, OH
Work style
On-site
Job type
Full time
Posted
Oct 19
3 days ago
Career path

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About the company

Avery Dennison

Packaging and containers · 10,001+ employees

Avery Dennison on LinkedIn

What you'll do

  • 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.

What you need

  • 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).
  • Education: a bachelor's degree

Key skills

Critical thinkingRoot-cause investigationsPredictive analyticsMinitabStatistical analysisProcess capability analysisAI productivity toolsMachine learningVerbal and written communication

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Read the full posting here
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: Exposure to process capability analysis (SPC/Cpk), spec-setting workflows, Lean Six Sigma concepts, or formal Root Cause Analysis (RCA). Pay Transparency The salary range for this position is $75,000 - $100,000/ year. The hiring base salary range above represents what Avery Dennison reasonably expects to pay for this position as of the date of this posting. Actual salaries will vary within the range, and in some circumstances may be above or below the range, based on various factors including but not limited to a candidate’s relevant skills, experience, education and training, and location, as well as the job scope and complexity, responsibilities, and regular and/or necessary travel required for the position, which may change depending on the candidate pool. Avery Dennison reserves the right to modify this information at any time, subject to applicable law. Equal Opportunity Employer Avery Dennison is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, protected veteran status, or any other protected status. Reasonable Accommodations Notice If you require accommodations to view or apply for a job, alternative methods are available to submit an application. Please contact (440) 534-6000 or NA.TA.Operations@averydennison.com to discuss reasonable accommodations. Identity Verification Notice To maintain the integrity of our hiring process and protect our candidates from identity fraud, we require video and/or in-person interviews. As part of this verification, we may also ask you to take a brief photo. This image is used exclusively to ensure identity consistency and will be permanently deleted once the hiring process concludes. Pre-employment Screening Notice As part of our commitment to maintaining a safe and productive work environment, Avery Dennison requires all final candidates to successfully complete a comprehensive background check and a drug screening. These checks are conducted post-offer and are a condition of employment. All results will be considered in accordance with applicable local, state, and federal laws.

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