AI-Driven Intern: Data Analytics & Workflow Optimization

Micron Technology, Inc

Singapore

On-site

SGD 20,000 - 36,000

Full time

4 days ago
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Job summary

Micron Technology, Inc. invites an intern to join the High Bandwidth Memory Product and Systems Engineering team in Singapore. You will map the end-to-end sample workflow, build AI-enabled predictions for sample arrival and reliability start dates, and forecast burn-in demand.

Expect dashboard development and cross-functional collaboration to drive data-driven improvements. The project focuses on hands-on exposure to semiconductor sample logistics, predictive analytics, BI, and workflow

Qualifications

  • Pursuing a Bachelor’s or Master’s degree in Industrial Engineering, Systems Engineering, Computer Science, Data Science, or related field.
  • Strong analytical and structured problem-solving abilities.
  • Familiarity with data analysis and visualization tools (Excel, Power BI, Tableau, Python, SQL).
  • Interest in semiconductor manufacturing, sample logistics, workflow optimization, and business intelligence.
  • Familiarity with machine learning or AI-enabled workflows is advantageous.

Responsibilities

  • Map end-to-end High Bandwidth Memory sample workflow from Die Bank to the Reliability Lab.
  • Develop AI-enabled models to predict sample arrival and reliability start dates.
  • Forecast burn-in resource demand and automate notifications.
  • Create Power BI or Tableau dashboards for work-in-progress tracking and capacity visibility.
  • Collaborate with engineering, planning, operations, and IT stakeholders; translate data insights into improvements.

Job description

Micron Technology, Inc. invites an intern to join the High Bandwidth Memory Product and Systems Engineering team in Singapore. You will map the end-to-end sample workflow, build AI-enabled predictions for sample arrival and reliability start dates, and forecast burn-in demand.

Expect dashboard development and cross-functional collaboration to drive data-driven improvements. The project focuses on hands-on exposure to semiconductor sample logistics, predictive analytics, BI, and workflow

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