- Hours
- Full time
- Where
- Boise, ID - Main Site
- Posted
- Sep 4
Our vision is to transform how the world uses information to enrich life for all.
Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.
Micron’s Mask Technology Center (MTC) develops and manufactures advanced photomasks that enable semiconductor device fabrication across Micron’s global manufacturing network. The Yield Enhancement team identifies, analyzes, and reduces yield loss through data-driven problem solving, statistical analysis, and continuous improvement.
The intern will improve manufacturing performance through advanced analytics, statistical modeling, automation, and AI-enabled problem solving.
What you'd do
- Analyze manufacturing, process, quality, and yield data to identify trends, anomalies, and improvement opportunities.
- Develop dashboards, visualizations, and automated reports that support operational decision-making
- Apply statistical methods and machine learning to investigate yield loss mechanisms and process variation
- Collaborate with Yield Enhancement, Process Engineering, Process Integration, and Manufacturing teams on process optimization.
- Deliver one end-to-end analytics project with measurable impact through yield improvement, cycle-time reduction, reporting automation, or improved decision support.
What they want
- Pursuing a BS, MS, or PhD in Data Science, Computer Science, Statistics, Applied Mathematics, Industrial Engineering, Engineering, or a related quantitative discipline.
- Senior undergraduate student or higher by the internship start date. Cannot graduate prior to September 2027.
- Demonstrated experience with Python, R, SQL, JMP, or similar analytics tools through coursework, research, or projects.
- Experience with statistical analysis, data modeling, or large dataset analysis.
- Ability to translate analytical results into clear, actionable engineering insights.
- Starts
- 2026-09-04