Senior Supply Chain AI & Analytics Engineer

Advanced Micro Devices, Inc•Austin, TX

8d•Hybrid

About The Position

At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. THE ROLE: In this highly visible role, you will help architect, develop, and operationalize AI‑driven analytics solutions that transform AMD’s end‑to-end supply chain. You will work across planning, logistics, inventory, transformation, and factory operations to turn complex challenges into actionable insights and autonomous decisioning. This is a senior individual-contributor role with future growth into people leadership, supporting the rapid expansion of AI capabilities within Supply Chain Transformation. You’ll partner globally across teams, influence technical and architectural decisions, and help drive clarity in a fast‑moving, ambiguous environment. THE PERSON: You thrive in dynamic, “gray‑area” environments and bring a balanced blend of technical depth, architectural thinking, and strong communication skills. You are comfortable managing shifting priorities, collaborating across functions, and working with global partners across time zones. You naturally provide structure and clarity, can manage upward, and enjoy enabling others as AI capabilities scale.

Requirements

Expertise in Python and/or R, with hands‑on experience building and deploying AI/ML models, generative solutions, or AI agents that automate insights, recommendations, or actions.
Strong capability in Power BI (data modeling, DAX, semantic layer design) and familiarity with ThoughtSpot or similar search/NLQ analytics tools.
Demonstrated ability to architect scalable data and analytics solutions, integrating models, pipelines, and AI agents with enterprise platforms and supply chain systems.
Background applying analytics, AI, or data science within planning, logistics, inventory, network optimization, S&OP, or other supply‑chain‑relevant workflows.
Experience with feature engineering, production pipelines, and deploying models/agents into repeatable, maintainable operating environments.
Knowledge of optimization techniques (linear programming, heuristics) and experience integrating analytics with ERP systems such as SAP.
Exposure to LLMs, prompt engineering, and enterprise agent frameworks, with the ability to design solutions that are explainable and user‑friendly.
Ability to work effectively in global teams, manage shifting priorities, collaborate across functions, and communicate clearly with leadership and technical partners.

Nice To Haves

Prior experience in a semiconductor supply chain is a plus but not required.

Responsibilities

Design, build, and deploy AI/ML models and advanced analytics that improve planning, inventory, logistics, network optimization, and S&OP, while architecting scalable data and analytics solutions that integrate seamlessly with enterprise platforms and supply chain systems.
Develop and operationalize AI agents that continuously monitor supply chain signals, surface proactive insights, recommend actions, and automate decision workflows to enable a more autonomous supply chain environment.
Translate ambiguous and complex supply chain problems into structured, data-driven solutions by performing exploratory analysis, scenario modeling, root‑cause investigations, and optimization across large datasets.
Create high‑quality dashboards, semantic models, and KPI frameworks in Power BI; enable search‑driven and natural‑language analytics through ThoughtSpot; and establish best practices for visualization and data storytelling.
Partner closely with data engineering teams to ensure reliable, well‑modeled datasets, while supporting pipeline development, feature engineering, and model deployment to production with an emphasis on scalability, explainability, and maintainability.
Collaborate across Supply Chain, IT, and Transformation teams to align analytics initiatives with business priorities; communicate insights effectively to technical and non‑technical audiences; mentor junior contributors; and operate fluidly in a global, fast-changing environment requiring flexibility across time zones.