Own the end-to-end NPI quality operating model for accessory programs, ensuring consistent phase-based deliverables, gate criteria, and launch readiness standards

Lead product and process risk management activities, including risk assessments, DFMEA, validation planning, and mitigation strategies to proactively identify and reduce quality risks

Partner with cross-functional stakeholders across Accessories & Apparel, Hardware Engineering, Supply Chain, Deployment, Global Support Services, and Quality to define and deliver accessory quality requirements

Drive root cause analysis and corrective and preventive actions for high-impact quality issues, using data-driven quality tools to prevent recurrence

Review quality control plans and partner with Supplier Quality Engineering and manufacturing teams to support implementation for accessory products

Develop data-driven insights, dashboards, and program reporting to guide quality decisions, track readiness, and support sustaining product quality activities

Bachelor’s degree in Engineering, Mechanical Engineering, Materials Science, or a related technical field

3–5+ years of experience in product quality, NPI quality, supplier quality, or product development quality, preferably within consumer electronics, wearable technology, wellness/fitness, medical device, or high-tech industries

Experience supporting quality activities across NPI or product development cycles, including validation planning, launch readiness, and sustaining quality support

Technical knowledge of accessory materials, components, and manufacturing processes, such as soft goods, metals, plastics, or wearable electronics components

Experience with DFMEA, risk assessment methodologies, validation planning, quality control plans, and root cause analysis tools

Strong ability to communicate quality insights and recommendations using objective data to influence cross-functional stakeholders at multiple levels of the organization

Familiarity with ISO 9001 Quality Management System standards, statistical data analysis, Six Sigma, or ASQ Certified Quality Engineer practices preferred

Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions