FE Supplier Quality Manager (Gas/Target)

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. HVM Phase Sourcing, Selection, and Qualification

• Lead quality risk assessments and implement mitigation strategies to support qualification activities.
• Deploy AI-based risk scoring models and digital dashboards to prioritize qualification risks and drive faster decision-making.
• Provide quality-focused input on sourcing and segmentation strategies, including business continuity planning, benchmarking, and supplier selection.
• Incorporate advanced analytics and AI-assisted scenario modeling into RFQ/RFI and supplier selection processes.
• Conduct technical risk assessment audits for new suppliers and materials to evaluate capability and readiness for HVM.
• Leverage digital audit tools and AI-assisted pattern recognition to identify systemic risks and hidden gaps.
• Establish, communicate, and ensure compliance with Supplier Requirements Standards (SRS).
• Support cross fab technology transfer activities in terms of new technology Bill of Materials (BOM) and driving global specifications alignment.

Supplier Development and Excursion Management

• Lead the definition and alignment of global material specifications for Target and Specs Gas categories
• Support development of smart specifications by integrating AI-driven process capability insights and predictive limits.
• Support Material Category Leads in shaping Category Quality Strategy, including Quality Roadmap and Defense Line Program.
• Embed AI/ML use cases into Quality Roadmap (e.g., predictive excursion detection, automated SPC monitoring, anomaly detection).
• Deploy Quality programs, lessons learnt and benchmark to/ with suppliers to ensure controls and preventive measures are in place to support Shift-Left-Strategy.
• Facilitate regular Quality and Technical Review (QTR) meetings with suppliers.
• Introduce data visualization and AI-assisted insights into QTRs to drive fact-based discussions and proactive actions.
• Manage supplier and sub-supplier changes in Supplier Change Management (SCM).
• Oversee resolution of globally impacting Target and Spec Gas quality issues.
• Utilize AI-driven root cause analysis tools (e.g., pattern mining, correlation analytics) to accelerate issue resolution.
• Collaborate with OCT MTE, Operations, and Facility teams for deep-dive investigations and implement corrective/preventive actions.
• Cascade lessons learned globally to ensure closed-loop prevention.
• Define, track, and monitor monthly supplier quality performance metrics and SRS compliance.
• Develop automated dashboards and AI-enabled performance monitoring systems for real-time supplier scorecards and predictive alerts.
• Continuously improve supplier quality processes to enhance productivity and efficiency.
• Partner with Procurement on supplier evaluations using quality metrics.
• Enable data integration across systems and apply AI analytics to drive objective, data-driven supplier decisions.

Communication & Presentation

• Provide weekly updates on the status and progress of all supplier-related activities.
• Maintain strong communication with Global Quality, Procurement, OCT, and cross-functional teams.
• Respond to issues promptly with appropriate escalation.
• Keep stakeholders informed of progress, risks, and opportunities.
• Leverage AI-assisted reporting tools to generate concise, data-driven insights and executive-ready summaries.
• Utilize visualization platforms and automated storytelling tools to enhance clarity and decision-making.

Training and Development

• Take ownership and accountability for achieving goals and completing actions.
• Establish clear, measurable personal development and performance objectives aligned with departmental and corporate priorities.
• Leverage guidance from coaches and mentors to build the skills necessary for your next career steps.
• Manage time and resources efficiently to maximize productivity.
• Continuously reflect on your performance, approach, and goals to drive improvement.
• Demonstrate a strong commitment to quality in all aspects of your work.
• Deliver assignments with accuracy, thoroughness, and timeliness.
• Show initiative and self-motivation in completing tasks, training, and projects.
• Continuously build AI/data literacy (e.g., understanding ML models, data pipelines, and digital tools) to enhance quality engineering effectiveness.
• Adopt and champion AI-enabled tools and digital workflows within SQE processes.

Growth Opportunity

• Collaborate with global SQEs and cross-regional teams on category initiatives.
• Serve as a key subject matter expert in supplier digitalization programs, contributing insights and guidance on AI/ML adoption in supplier quality.
• Lead or participate in development of AI use cases (predictive quality, smart SPC, automated audits, supplier risk intelligence).
• Build deep expertise in material criticality by acting as a primary liaison with suppliers.

Drive innovation in digital quality ecosystems, including data integration, automation, and AI-enabled supplier collaboration platforms. Education: Master’s degree or bachelor’s Degree in Chemical/ Chemistry/ Material Engineering or Related Field. Experiences:

• Minimum 5 years of experience in the semiconductor industry or a high-volume manufacturing environment is highly advantageous.
• Proven experience in supplier quality management, with the ability to effectively manage and drive performance improvement across global suppliers and international supply chains.
• Demonstrated ability to collaborate effectively with cross-functional and global teams, including Manufacturing, Procurement, Engineering, Quality, and Supply Chain organizations.
• Strong problem-solving, stakeholder management, and communication skills with the capability to influence and drive quality improvements across a global supplier network.
• Exposure to incoming material control (IQC), or manufacturing quality processes is an added advantage. Experience in semiconductor manufacturing processes, wafer fabrication, or related process engineering disciplines is preferred.
• Basic familiarity with ISO 9001 and IATF 16949 quality management system standards and their application in manufacturing environments.
• Ability to utilize data analytics and AI insights to assess material risks, performance trends, and variability.
• Experience leveraging digital/AI-driven tools to improve quality performance, yield, or supplier management is a plus.

Qualifications:

• Proficient in Microsoft Office Suite (PowerPoint, Word, Excel, OneNote).
• Experience with data visualization tools (e.g., Power BI) and AI-assisted productivity tools to generate insights, dashboards, and executive summaries.
• Demonstrated ability to work independently in a dynamic, fast-paced environment.
• Ability to apply data-driven and AI-assisted decision-making with minimal supervision.
• Effective communicator with ability to engage stakeholders at all levels.
• Skilled in translating AI/data insights into clear, actionable recommendations for technical and non-technical audiences.
• Strong analytical capabilities, with hands-on experience in:
• Measurement System Analysis (MSA) and calibration
• Statistical Process Control (SPC) and FMEA
• Problem-solving methodologies
• Quality management systems
• Application of advanced analytics or AI/ML techniques (e.g., anomaly detection, predictive modeling, trend analysis) to enhance root cause analysis and process control.
• AI/ML & Digital Experience (Preferred)
• Experience applying AI/ML models or advanced analytics to quality analytics, supplier performance monitoring, or excursion management.
• Familiarity with data pipelines, automation, and digital dashboards for real-time monitoring and decision-making.
• Demonstrated ability to integrate multiple data sources (supplier, fab, metrology) to drive insights and continuous improvement.

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