Customer Engineer - Data Science / ML DevOps Internship

Applied Materials, Inc.; Singapore; On-site; SGD 13,000 - 20,000; Full time

Job summary

Applied Materials, Inc. in Singapore invites an intern to explore model governance and end-to-end model management. You will collaborate with data scientists and equipment experts to pilot a framework and document best practices in a live production setting.

Preferred disciplines include Data Science & Analytics or Computer Engineering. Key skills: Python, basic ML, data visualization, and the ability to interpret experimental results and draw conclusions.

Qualifications

• 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.

Skills

Python

ML basics

Data analysis

Physics concepts

Data visualization

Experiment interpretation

ML DevOps

Education

Data Science

Computer Engineering

Job description

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.

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

Preferred Discipline: Data Science & Analytics Computer Engineering

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 time Employee Type: Intern / Student Travel: No Relocation Eligible: No

Equal Opportunity Employer Statement

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.