Senior Manager, Supply Chain Data Science & AI Operationalization

Key Responsibilities

• 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

• Understands supply chain economics (procurement, inventory, repair and reverse value chain, order fulfillment, supplier performance); anticipates business issues and recommends process, product or service improvements.

Leadership

• Leads projects with notable risk and complexity; develops the strategy for project execution and mentors analysts to raise the delivery bar.

Problem Solving

• Solves unique and complex problems with broad business impact; reframes recurring manual analyses as automatable, governed capabilities rather than point solutions, using conceptual and innovative thinking.

Impact

• Impacts the direction and resource allocation for programs and projects; delivers governed, AI-assisted tools that compound in value across the supply chain, within general functional policies and industry guidelines.

Interpersonal Skills

• Communicates complex ideas, anticipates objections and persuades others, often at senior levels, to adopt a different point of view; partners with function stakeholders as collaborators, not ticket submitters.

About the role

A hands-on delivery leadership role. Applied Materials' Global Service Operations moves billions of dollars of parts, repairs and inventory across Procurement, Order Fulfillment and the Reverse Value Chain. The analytics that steer those decisions today are strong, and ready to be amplified with AI at scale. You will turn the function's roadmap into shipped, governed, AI-assisted tools, building where it counts, leading a small team of analysts, and holding delivery to clear cycle-time and quality standards.

First-year outcomes

• The highest-value analyses in your domain converted from one-off manual work to repeatable, governed, AI-assisted workflows.
• Key tools graduated from notebooks to governed production, each with lineage, monitoring and a named owner.
• Delivery consistently meeting published cycle-time and quality SLAs, with measurable reduction in turnaround.
• A small analyst team leveled up, with raised standards and reduced key-person risk.

Required qualifications

• Hands-on experience delivering AI and analytics in a supply chain context, with measurable business results (dollars, cycle time or hours reclaimed).
• Strong builder: has taken analytics from notebooks to governed production with lineage, monitoring and clear ownership.
• Deep technical foundation: SQL, Python, Databricks / lakehouse, enterprise BI.
• Applied AI tooling: LLMs and generative AI (RAG, agentic workflows), MLflow or equivalent, AI-assisted development tools.
• Experience deploying governed services on an enterprise application / MLOps platform such as ARO (Azure Red Hat OpenShift) or equivalent (Kubernetes / OpenShift, Azure ML, cloud-native).
• People leadership: has led or mentored analysts and managed delivery against demand.
• Partners with function stakeholders to translate their decisions into analytics they act on.

Preferred qualifications

• Semiconductor, high-tech or complex global supply chain experience (parts, repair, reverse logistics, order fulfillment).
• Domain depth in reverse value chain / repair, inventory optimization, supplier performance or on-time-delivery analytics.
• Contributed to standing up an AI / analytics center of excellence or MLOps practice.

Education and experience

• Bachelor's degree required; advanced quantitative degree preferred. 8 to 12 years in data science / advanced analytics, including hands-on supply chain analytics and AI delivery and some team leadership.

## Qualifications

### Education:

Bachelor's Degree

### Skills

### Certifications:

### Languages:

### Years of Experience:

10 - 15 Years

### Work Experience:

## Additional Information

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### Shift:

10-Day 8-Hr (United States of America)

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### Travel:

Yes, 10% of the Time

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### Relocation Eligible:

No

### Referral Payment Plan:

Employee Referral (Standard)

Salary Range:

$193,500.00 - $266,000.00

The salary offered to a selected candidate will be based on multiple factors including location, hire grade, job-related knowledge, skills, experience, and with consideration of internal equity of our current team members. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program, as applicable.

For all sales roles, the posted salary range is the Target Total Cash (TTC) range for the role, which is the sum of base salary and target bonus amount at 100% goal achievement.

Applied Materials is an Equal Opportunity Employer committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.