Data Scientist - Smart Manufacturing & AI

About the role

Micron Technology, a global leader in memory and storage solutions, is hiring a Data Scientist (Smart Manufacturing & AI) to develop data-driven solutions that improve semiconductor manufacturing yield, quality and productivity. You will work alongside senior data scientists and product owners to build predictive models, AI-powered solutions and analytical pipelines that handle die-level, wafer-level and test data.

Key responsibilities

• Build and productionize machine learning models (classification and regression) for predictive and prescriptive analytics across semiconductor test and manufacturing processes.
• Contribute to multi-agent AI systems for TSE workflows, including code generation, automated troubleshooting and process optimization.
• Work with retrieval-augmented generation (RAG) pipelines: identify data sources, develop retrieval strategies and improve context quality for LLM applications.
• Assist in implementing tool-using capabilities, function calling and agent memory systems.
• Develop maintainable code and analytical pipelines optimized for performance, latency and token efficiency, and collaborate on testing, deployment and monitoring.
• Communicate analytical insights and model results clearly to technical and non-technical stakeholders.

Required profile

• Bachelor's degree in Computer Science, Data Science, Operations Research, Mathematics or equivalent.
• Strong desire to grow a career as a Data Scientist in advanced, highly automated industrial manufacturing.
• Clear verbal and written communication skills, able to explain complex analytical results.

Required skills

• Proficiency in Python for data analysis and modeling.
• Solid foundation in statistics and/or machine learning (supervised/unsupervised learning, model evaluation, feature engineering).
• SQL for data extraction and manipulation, and Git for version control.
• Familiarity with interactive data applications or dashboards (e.g. Streamlit, PowerBI).
• Ability to use AI-enabled tools responsibly and effectively.

Preferred: exposure to LLMs, agentic AI or RAG pipelines; time-series or manufacturing data; cloud platforms (GCP, AWS, Azure) and visualization tools (Tableau, Power BI); ETL tools, Docker or web frameworks (Angular, React, FastAPI); coursework in optimization or statistical modeling.