Intern- MSB Process Integration Engineer

Our vision is to transform how the world uses information to enrich life for all. Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing. Location Singapore Department MSB Process Integration Engineering Project Title Semiconductor Manufacturing Variability Reduction Using Advanced Data Analytics Project Description This project provides the intern with an opportunity to learn how advanced data analytics can be applied to reduce process variability and reject rates in semiconductor manufacturing. The intern will analyze inline signals, process parameters, and test results to identify patterns associated with Assembly and Test issues. The project will provide practical exposure to semiconductor packaging processes, yield analysis, statistical methods, Artificial Intelligence, and cross-functional engineering collaboration. Objective of the Project The objective is to develop data-driven insights and analytical methods that: Identify potential sources of manufacturing variability and yield loss. Improve the analysis of inline signals, process data, and test results. Detect relationships between process conditions and product rejects. Recommend opportunities for improving process stability and manufacturing quality. Explore AI-Enabled methods that enhance analytical efficiency and effectiveness.   Project Scope The intern will be given the opportunity to: Learn semiconductor packaging, Assembly and Test processes, and common sources of process variability and yield loss. Analyze inline signals, process parameters, manufacturing data, and test results using Python, Structured Query Language, or equivalent analytical tools. Apply statistical and data analytics techniques to identify patterns, correlations, and potential contributors to reject rates. Explore AI-Enabled tools and analytical methods to improve the efficiency, quality, and effectiveness of the project analysis. Document findings and present data-driven recommendations to engineering stakeholders.   Learning Opportunities The intern will have the opportunity to: Gain practical exposure to semiconductor packaging and high-volume manufacturing. Learn how manufacturing variability and yield loss are investigated using process and test data. Apply programming, statistical analysis, visualization, and structured problem-solving techniques. Develop responsible proficiency in AI-Enabled tools and workflows in accordance with organizational standards. Strengthen technical documentation, stakeholder communication, and cross-functional collaboration skills.   Deliverables The intern is expected to deliver: A structured analysis of relevant inline signals, process parameters, and test results. Identification of key patterns and potential contributors associated with process variability and reject rates. Data visualizations or analytical models that communicate findings clearly. Documented recommendations and proposed next steps for engineering evaluation. A final technical report and presentation covering the methodology, findings, limitations, and recommendations.   Impact of the Project The project is intended to: Improve visibility into manufacturing variability and sources of yield loss. Enable more effective analysis of Assembly and Test issues. Provide data-driven insights for process stability and quality improvement. Strengthen the use of advanced analytics and AI-Enabled workflows in semiconductor manufacturing. Establish reusable analytical methods for future variability-reduction projects.   Skillsets Required The ideal candidate should possess: Programming knowledge in Python, Structured Query Language,