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Principal Data Scientist, TPG at Micron Technology
Boise, Idaho, United States -
Full Time

Start Date

Immediate

Expiry Date

19 Feb, 26

Salary

0.0

Posted On

21 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Data Science, Semiconductor Industry, Machine Learning, Data Engineering, UI/UX, ML Ops, Large Language Models, Fine-Tuning, Prompt Engineering, Agentic AI Systems, Python, R, Communication Skills, Interpersonal Relationships, AI Solutions, Collaboration

Industry

Semiconductor Manufacturing

Description
BS + 10 years, MS + 5 years or PhD + 3 years of data science and / or semiconductor industry experience majoring in Engineering, Mathematics, Computer Science, Data Science, Physics, or equivalent Experience in semiconductor industry preferred. Experience in the memory industry is highly desirable. Expertise developing and deploying data science solutions including data engineering, application of Machine Learning algorithms, UI/UX, and ML Ops. Experience working with Large Language Models (LLMs), including fine-tuning, prompt engineering, and evaluation of model performance. Familiarity with agentic AI systems—designing, deploying, or integrating autonomous agents capable of reasoning, planning, and executing tasks across complex workflows. Proficient in program languages such as Python or R. Excellent written and verbal English-language communication and presentation skills. Ability to build strong interpersonal relationships, and a passion to teach others about data science and continue to learn new techniques. Develop and implement AI and ML solutions to support product development, validation, and optimization. Collaborate with functional subject matter experts across engineering teams to understand domain problems and identify data science solutions. Stay up-to-date with the latest advancements in data science and apply them to solve complex problems.
Responsibilities
Develop and implement AI and ML solutions to support product development, validation, and optimization. Collaborate with functional subject matter experts across engineering teams to understand domain problems and identify data science solutions.