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Advanced Packaging Yield and Data Analytics Director

The Role:

We are seeking a highly skilled and visionary Advanced Packaging Yield and Data Analytics Director to lead our efforts in advanced packaging yield enhancement and next-generation data analytics innovation. This role is at the intersection of semiconductor manufacturing, AI-driven analytics, and heterogeneous integration, with a focus on enabling best-in-class yield performance across AMD’s portfolio- including client, server, gaming, and AI products. The ideal candidate is both a data scientist and a yield technologist, capable of shaping and executing a roadmap that drives yield improvements while defining and deploying cutting-edge analytics and machine learning tools. This individual will work closely with design, R&D, operations, and supply chain partners worldwide to define Design for Manufacturing (DFM) and Design for Yield (DFY) standards and develop scalable analytics systems across AMD’s diverse advanced packaging ecosystem.

Key Responsibilities:

• Lead yield improvement initiatives across advanced packaging technologies (2.5D, 3D-IC, fan-out, etc.) for AMD’s high-performance computing products
• Define and implement yield prediction tools and methodologies for heterogeneous integration
• Collaborate with product design teams and technology development partners to incorporate DFM/DFY practices
• Develop a comprehensive roadmap for AI and analytics tools tailored to advanced packaging yield challenges
• Partner with internal IT and external analytics providers to build and deploy scalable, automated yield tracking and decision-making systems
• Engage with global OSATs, substrate suppliers, and foundry partners to identify and resolve yield pain points using data-driven approaches
• Evangelize data analytics capabilities and mentor engineers in best practices for yield analytics and AI-driven insights
• Contribute to AMD’s system technology co-optimization strategy through deep data integration across design and manufacturing

The Person:

You are an experienced technical leader with a hands-on approach to data science and semiconductor manufacturing. You bring a strong foundation in statistical analysis, machine learning, and advanced packaging technologies, and you're adept at leading cross-functional teams toward complex yield and analytics goals. You possess excellent communication skills and can distill complex data insights into actionable recommendations for engineering and executive stakeholders alike.

Preferred Experience:

• 15+ years in semiconductor packaging with a focus on advanced architectures (e.g., 2.5D, 3D die stacking, chiplet integration)
• 5+ years in a leadership role managing yield engineering or data science teams
• Proven experience deploying machine learning and data analytics to drive yield improvements
• Hands-on expertise with SQL, Python, JMP/JSL, and analytics environments (e.g., Jupyter Notebooks)
• Experience with cloud-based data warehouse and analytics platforms (e.g., AWS, Azure, Snowflake) is a strong plus
• Deep familiarity working with external manufacturing partners, including OSATs, substrate vendors, and foundries in driving yield improvements through data analytics.
• Proven track record of driving Advanced packaging solutions from R&D to HVM

Academic Credentials:

• BS/MS/PhD in Computer Science, Applied Mathematics, Electrical Engineering, Mechanical Engineering, or related technical discipline

Location: Austin, TX

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AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.