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Applied Materials

College Intern

Posted Yesterday
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In-Office
Singapore, SGP
Internship
In-Office
Singapore, SGP
Internship
Develop an AI-enhanced Power BI dashboard integrating supplier, subtier, forecast, and capacity data. Build predictive models to identify supply chain delivery gaps, single-point-of-failure risks, capacity constraints, and geographical or sourcing dependencies. Analyze demand trends and create early-warning indicators and scenario analyses that support supplier account management, proactive risk mitigation, and resilient supply chain planning.
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Who We Are


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. 


What We Offer


Location:

Singapore,SGP

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

AI-Driven Subtier Supplier Risk & Capacity Visibility Dashboard

Preferred Discipline:

  • Business Analytics /Supply Chain Analytics (Preferred)

Traineeship Project Description:

The intern will develop a data-driven solution to improve visibility into supply chain subtier capacity, forecast demand, and potential single-point-of-failure (SPOF) risks.

The project will focus on integrating suppliers, subtier, forecast, and capacity data into a centralized Power BI dashboard, enhanced with AI/ML predictive models to identify potential delivery gaps before they impact production.

The solution will analyze demand trends, supplier capacity constraints, geographical dependencies, and sourcing concentration to provide early-warning indicators and actionable insights for Supplier Account Management team.

The objective is to have zero last-minute surprises caused by subtier shortages and improve proactive risk mitigation through better forecasting and scenario analysis.

Desired Skills Required:

  • Business Analytics /Supply Chain Analytics (Preferred)
  • Power BI dashboard development and data modeling
  • Python, SQL, or data engineering fundamentals
  • Machine Learning / Predictive Analytics
  • Data visualization and storytelling

Strong problem-solving and stakeholder communication skills

Learning Outcome:

  • Apply business analytics techniques to solve real-world supply chain challenges.
  • Build end-to-end Power BI solutions using multiple data sources.
  • Develop predictive models for forecasting supply chain risks and capacity constraints.
  • Understand multi-tier supply chain structures, supplier risk management, and resilience strategies.
  • Generate actionable insights that support proactive decision-making and risk mitigation.

Gain practical experience in AI-enabled supply chain planning and digital transformation initiatives.

Additional Information

Time Type:

Full time

Employee Type:

Intern / Student

Travel:

No

Relocation Eligible:

Yes

Applied Materials is an Equal Opportunity Employer. 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.

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