About the role

AI summarised

The Senior Product Analytics Engineer at Micron Technology develops and deploys machine learning and AI solutions to improve semiconductor yield, reliability, and test efficiency. This role involves collaborating with product engineering, fab, and design teams to build data-driven workflows for NAND product development and enabling Citizen Data Scientists through mentorship and training. The position requires strong Python and ML skills, semiconductor domain knowledge, and the ability to translate data insights into engineering actions.

IDMOnsiteSTPG

Key Responsibilities

Apply advanced ML techniques on large‑scale structured semiconductor datasets, including wafer fab inline data, probe data, and test data
Build, validate, and maintain end‑to‑end ML workflows supporting NPI and HVM decision‑making
Use data‑driven methods to support root cause analysis of yield, reliability, and defectivity issues
Design and implement agentic AI solutions to automate Product Engineering workflows (e.g. Yield and reliability analysis, test program validation and screening optimization, engineering report generation and data summarization)
Integrate AI agents with enterprise and engineering systems (e.g., JIRA, Confluence, SharePoint, internal analytics platforms)
Work closely with Fab, NAND Technology Development, NAND Design, Test Solutions, Quality/Reliability, and System teams to address reliability and defectivity failures during qualification
Support yield improvement, cost reduction, and test optimization initiatives
Drive data‑backed technical decisions for NAND product releases
Mentor and guide Citizen Data Scientists within Product Engineering on applying machine learning techniques to real engineering problems with protocols embraced
Build structured learning paths, guidelines, and hands‑on training modules for ML and advanced analytics

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical / Electronic Engineering
Minimum 4 years of ML/AI experience
Strong proficiency in Python and data processing libraries (Pandas, NumPy, Scikit‑learn)
Hands‑on experience with ML models for structured/tabular data, including regression, tree‑based models, and gradient boosting
Ability to build, evaluate, and interpret ML models for engineering decision‑making
Experience with ML/DL frameworks (TensorFlow, PyTorch) for images, text, logs, or unstructured data
Exposure to LLM‑based and agentic AI frameworks (e.g., LangChain, Microsoft Copilot Studio)
Familiarity with cloud‑based AI platforms (e.g., Azure ML, AWS SageMaker)
Semiconductor / NAND manufacturing or product engineering experience
Strong analytical and problem‑solving skills
Excellent communication skills with the ability to translate data insights into engineering actions
Ability to work effectively in fast‑paced, cross‑functional environments