Base: $23.75 - $54.30ph; bonus/equity: not specifi...
Extract and mine large datasets
Structure manufacturing data into jmp-ready formats
Perform spc analysis
This internship focuses on mining production line data, structuring it into JMP-compatible formats, and enabling engineers to perform statistical process control (SPC), Cp/Cpk analysis, and yield correlation studies
Job Summary
This internship focuses on mining production line data, structuring it into JMP-compatible formats, and enabling engineers to perform statistical process control (SPC), Cp/Cpk analysis, and yield correlation studies.
You will work closely with manufacturing, process, and product engineers to identify key drivers of yield loss and process instability across assembly and test operations.
We're committed to bringing passion and customer focus to the business. If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
Matching Summary
This internship focuses on mining production line data, structuring it into JMP-compatible formats, and enabling engineers to perform statistical process control (SPC), Cp/Cpk analysis, and yield correlation studies.
Salary
Base: $23.75 - $54.30/hour; Bonus/Equity: Not specified; Benefits: Not specified
Skills & Requirements
Must-have
Extract and mine large datasets
Structure manufacturing data into JMP-ready formats
Perform SPC analysis
Calculate and interpret Cp/Cpk metrics
Correlate process parameters with yield
Develop automated Python scripts
Nice-to-have
Proactive, hands-on mindset
Interest in manufacturing process improvement
Collaborate with cross-functional teams
Enthusiastic over-achievers
Key Requirements
Currently enrolled in Bachelor's or Master's degree
Industrial Engineering, Electrical Engineering, Mechanical Engineering, Data Science, or related technical field
Experience with statistical analysis and process capability concepts
Proficiency in Python (Pandas, NumPy)
Familiarity with JMP or other statistical process control software
Exposure to manufacturing systems, MES databases, or SQL querying
Understanding of yield analysis and process variation reduction