define roadmaps, establish procedures, and lead initiatives for technology transfer into high-volume manufacturing
optimize processes, improving yield, and driving quality initiatives
Analyze structured and unstructured data, applying statistical methods and machine learning techniques, with expertise in tools such as SQL and Python
Perform low-yield analysis and drive yield improvement activities
Lead continuous improvement programs to sustain quality
Requirements
A Ph.D. in Electrical Engineering, Electrical Computer Engineering, Chemical Engineering, Material Science, Physics, Chemistry, or a Semiconductor-related STEM field of study (with a focus on hands-on experimental research)
OR a Master's degree candidates should have 2+ years of experience in semiconductor manufacturing or semiconductor device related field
Advanced degrees in relevant technical fields such as engineering or data science.
Track record of driving change and continuous improvement within high-tech manufacturing or semiconductor environments.
Experience with data analysis software and techniques and design of experiments