Senior Data Scientist

Our vision is to transform how the world uses information to enrich life for all.
Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever.

As a Senior Data Scientist (Smart Manufacturing and AI) at Micron, you will leverage advanced techniques from mathematics, statistics, and information technology to improve semiconductor product quality, yield, and productivity. You will develop predictive models, actionable insights, and AI-enabled solutions that address large-scale manufacturing and quality challenges involving die-level, wafer-level, test, and quality/customer-related data.

Your work will directly help prevent customer-impacting quality events, reduce escapes, accelerate root-cause analysis and corrective actions, detect distribution shifts in manufacturing and field signals, and enable intelligent automation across Micron’s global fabs, bridging factory learning with customer-facing product quality outcomes.

Responsibilities and Tasks

• Product Quality (PQ) Predictive Solutions:

• Build and productionize ML models (classification/regression) for product-quality prediction (e.g., escape prevention, failure-mode prediction, risk scoring) across manufacturing and post-manufacturing data.

• Develop anomaly and drift detection/monitoring (e.g., Isolation Forest, autoencoders/VAEs) to identify emerging quality signatures in fab, test, and field/customer signals.

• Apply text analytics (embeddings, clustering, classification, summarization) to quality narratives, customer feedback, FA notes, and engineering logs.

• Agentic AI Development:

• Design and implement multi-agent systems for PQ workflows, including automated root-cause analysis and process optimization.

• Implement tool-using capabilities, function calling, agent memory systems, and agent skills.

• Implement comprehensive monitoring, logging and observability for agentic systems.

• RAG-LLM Development:

• Identify potential data sources for RAG implementation.

• Develop advanced retrieval strategies including hybrid search, re-ranking, and contextual compression.

• Software Engineering and Productionalization:

• Develop robust, maintainable software and analytical pipelines suitable for high-volume manufacturing environments.

• Implement guardrails, hallucination detection, and output validation.

• Optimize system performance, latency, token and cost efficiency.

• Guide and mentor junior data scientists, driving best practices in code quality, system architecture and applied AI research.

• Clearly communicate analytical insights, model behavior, and trade-offs to both technical and non-technical customers to enable data-driven decisions.

• Integrates AI-assisted tools and insights into daily work to improve efficiency, quality, or effectiveness, exercising sound judgment and complying with organizational standards and legal requirements.​

• Contributes to a culture of continuous improvement by identifying, testing, and sharing AI-enabled enhancements within one’s scope of work.​

Required Qualifications & Skills

• Education/Experience :

• Master's or Ph.D. in Computer Science, Data Science, Operations Research, Mathematics, or a related highly quantitative field.

• 5+ years of industry experience in Data Science, with a proven track record of developing and deploying machine learning and/or large language model solutions that solve complex business problems at scale.

• Or equivalent combination of education and experience.

• Technical Skills:

• Proficiency in Python and experience extracting and manipulating data using SQL.

• Hands-on experience with LLMs and agentic frameworks (e.g., Google ADK, or similar) including prompt engineering, retrieval-augmented generation (RAG), and tool/function calling.

• Experience building interactive data applications and APIs (e.g., Streamlit, FastAPI, Flask).

• Experience working in Agile/SDLC environments with version control (Git) and collaboration tools (JIRA, Confluence, etc.).

• Strong verbal and written communication skills, with the ability to explain complex analytical results clearly.

• Ability to apply baseline digital fluency and role‑appropriate AI literacy to use AI‑enabled tools responsiblyand effectively for research, analysis, content creation, problem‑solving, operational tasks, and achieving business outcomes

Preferred Qualifications

• 2+ years building production LLM or agentic AI systems (multi-agent workflows, memory systems, orchestration, monitoring, guardrails).

• Exposure to manufacturing or process data (time-series, concept drift, high-dimensional sensor/test data, imbalanced labels).

• Familiarity with cloud ML platforms (Google Cloud Vertex AI) and containerization (Docker, Kubernetes).

• Experience with ETL/data integration tools and UI frameworks (e.g., Angular, React).