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,SGPYou’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.
Project Description:
Predictive models provide value for field service engineers to be able to move from break-fix ways of maintenance to a more proactive maintenance approach. Accordingly, as the number of models increase, model governance and management become crucial to sustain the value of the models to the end users. The intern will study the existing approach and propose improvements to the current situation. The intern will collaborate with data scientists and equipment experts to pilot a framework for end to end model management along with documentation of best practices.
Preferred Discipline:
Data Science & Analytics
Desired Skills Required:
Programming in Python; Basic knowledge of machine learning and data analysis; Understanding of physics principles related to sensing and measurement; Familiarity with data visualization tools; Ability to interpret experimental results and draw conclusions. Familiarity with machine learning workflows and ML DevOps best practices.
Learning Outcome:
The intern will gain practical experience in process improvement methodologies and digital transformation within an operational setting. They will develop skills in stakeholder engagement, data collection, and analysis, and will learn how to translate process insights into actionable recommendations. They will gain firsthand experience applying industrial engineering methodologies in a live production environment, learn to present data-backed recommendations to stakeholders, and understand how systematic analysis contributes to operational efficiency and lean manufacturing goals
Additional Information
Time Type:
Full timeEmployee Type:
Intern / StudentTravel:
NoRelocation Eligible:
NoApplied 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.