About the role

AI summarised

Senior Product Analytics Engineer at Micron Technology, a semiconductor memory and storage leader. The role focuses on developing and deploying machine learning models and agentic AI solutions to improve yield, reliability, and test efficiency in NAND product engineering. Responsibilities include building ML workflows, automating engineering tasks, collaborating with cross-functional teams, and mentoring citizen data scientists.

IDMFull-timeSTPG

Key Responsibilities

Develop and deploy predictive and diagnostic ML models for Yield improvement, test time reduction (TTR) and cost optimization, NAND reliability analysis, and Qualification.
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, and drive data-backed technical decisions for NAND product releases.
Apply strong understanding of NAND manufacturing flows, defect mechanisms, and reliability requirements to ensure AI solutions are domain-relevant and impactful.
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.
Review and provide technical guidance on AI/analytics projects to ensure quality, rigor, and business impact.

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, Electrical / Electronic Engineering with min 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.