Data Scientist, Smart Mfg & AI

Job Summary As a Data Science Engineer at Micron, you will employ techniques and theories drawn from areas of mathematics, statistics, semiconductor physics, materials science, and information technology to uncover patterns in data from which predictive models, actionable insights, and solutions can be developed. You will interact with experienced Data Scientists, Data Engineers, Business Areas Engineers to identify questions and issues for data analysis projects and improvement of existing tools. In this position, you will help develop software programs, algorithms and/or automated processes to cleanse, integrate, and evaluate large datasets from multiple disparate sources. There will be significant opportunities to perform exploratory and new solution development activities. Responsibilities include, but are not limited to: Improve equipment efficiency, factory productivity, and MaxOut performance through data science and AI solutions. Develop machine learning, simulation, and optimization models for manufacturing applications. Analyze manufacturing, equipment, and process data to identify improvement opportunities and drive operational excellence. Build scalable analytics products, dashboards, and automated workflows. Partner with cross-functional engineering teams to deliver high-impact manufacturing solutions. Present insights and recommendations to stakeholders to enable data-driven decision making. Requirements: Min Bachelor's degree in computer science, data science, statistics, mathematics, or engineering. Strong desire to grow a career as a Data Scientist in highly automated industrial manufacturing Experience in statistical modeling, feature extraction and analysis, and various machine learning methods (supervised, unsupervised, semi-supervised). Strong communication skills (written, verbal and presentation). Strong software development skills Experience with advanced analytics or AI-driven analysis for manufacturing is preferred Leverage AI-enabled tools and technologies to improve productivity, automate workflows, and accelerate solution delivery. Required Soft Skills Effective communicator and collaborator, capable of bridging the gap between data science and traditional semiconductor engineering teams. Analytical and problem-solving mindset with a demonstrated commitment to quality and continuous improvement in a fast-paced environment. Proven ability to work independently, manage multiple priorities, and deliver high-quality results.