Industrial Engineering Intern: Drive Efficiency with Data

Entegris, Inc.

Kuala Lumpur

On-site

MYR 17,000 - 23,000

Part time

6 days ago
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Benefits offered by this job

Paid holidays 17 days
Generous PTO
Values-driven culture

Job summary

Entegris, Inc. is seeking an Industrial Engineering Intern in Malaysia to support the GMC Area Study Project.

You will assist in operational analysis, data-driven decision making, and continuous improvement initiatives to enhance productivity and efficiency within the GMC area. You’ll conduct cycle time studies, collect and analyze production data, and collaborate with cross-functional teams to validate findings and implement recommendations.

Qualifications

  • Pursuing a Bachelor’s degree in industrial or related engineering fields.
  • Strong analytical and problem-solving skills; able to work with data.
  • Proficient in Excel and data analysis tools; good communication skills.

Responsibilities

  • Conduct cycle time studies and evaluate current processes.
  • Collect, organize, and analyze production and workflow data to identify trends.
  • Support routing analysis and recommend improvements for efficiency.
  • Assist in productivity improvement projects in the GMC area.
  • Collaborate with engineers, supervisors, and cross-functional teams.
  • Prepare reports and presentations of study results and recommendations.
  • Participate in continuous improvement initiatives and support implementation.

Skills

Analytical thinking
Problem solving
Communication
Self-motivated

Education

Bachelor's degree in Industrial Engineering, Manufacturing Engineering, or Mechanical Engineering

Tools

Microsoft Excel
Data analysis tools

Job description

Entegris, Inc. is seeking an Industrial Engineering Intern in Malaysia to support the GMC Area Study Project.

You will assist in operational analysis, data-driven decision making, and continuous improvement initiatives to enhance productivity and efficiency within the GMC area. You’ll conduct cycle time studies, collect and analyze production data, and collaborate with cross-functional teams to validate findings and implement recommendations.

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