Senior Manager, Supply Chain Data Science & AI Operationalization

Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world’s technology.

Salary: $193,500.00 - $266,000.00 Location: Santa Clara,CA You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more.  At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits .

Drive strong AI operationalization as the core of the role: embed AI, generative AI and LLM-based methods into how supply chain analytics is produced, turning manual, one-off analyses into governed, automated, always-on workflows that scale output without adding headcount. Lead delivery of supply chain analytics within Applied Materials' Global Service Operations (Procurement, Order Fulfillment, Reverse Value Chain and supplier operations), owning execution against the function's roadmap and delivery SLAs. Build and ship the repeatable, AI-assisted workflows and agents that convert recurring manual work into reusable, governed tools, hands-on where it counts. Take high-value tools from prototype to governed production (Databricks, BI hosting, enterprise application / MLOps hosting) with data lineage, monitoring, model lifecycle management and clear ownership. Establish and apply AI operationalization practices: prompt and model evaluation, human-in-the-loop controls, versioning, monitoring and governance, so AI outputs are trusted and decision-ready. Plans, manages and controls the activities of a team of analysts that provides business intelligence and strategic planning support for the business. Leads project teams to design and develop the methods, processes and systems that consolidate and analyze structured and unstructured, diverse "big data" sources; communicates insights and findings to business management to improve business processes. Performs and directs complex statistical and data-mining analysis; provides input on and design of data acquisition systems, data structure and database design. Brings expertise or identifies subject-matter experts in support of multi-functional efforts to identify, interpret and produce recommendations based on company and external data. Helps business groups understand their data; applies analytics to derive insights and works with the business to determine actions and KPIs for those actions. Selects, develops and evaluates personnel, ensuring efficient operation of the function. Leads or participates in project teams developing analytical models, algorithms and automated processes, applying SQL and Python, to cleanse, integrate and evaluate large datasets. Tracks delivery cycle time, quality and business impact (dollars influenced, hours reclaimed) and reports to the function lead. Functional Knowledge Regarded as the technical expert in supply chain analytics and AI operationalization: SQL, Python, Databricks / lakehouse, BI, and applied AI / LLMs (RAG, agentic workflows) used to productionize workflows. Demonstrates in-depth expertise in own discipline and broad knowledge of other disciplines within the function. Business Expertise