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, and UX teams 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.

Key Responsibilities
Collaborate with the engineering, product and design teams to supply data science support for product vision and business decisions
Build processes that enable our teams to be autonomous and outcome-driven
Influence the broader product direction by providing guidance and expertise on data science and machine learning best practices
Design and evaluate novel approaches for handling high-volume real-time datasets

Qualifications
Master's degree in Computer Science, Engineering, or a related field.
Proven experience in developing AI tools, with a focus on generative models.
Strong knowledge of NLP techniques and frameworks (e.g., Transformers, BERT, GPT).
Strong desire to grow a career as a Data Scientist in highly automated industrial manufacturing doing analysis and machine learning on terabytes and petabytes of diverse datasets.
Experience in the areas: statistical modeling, feature extraction and analysis, supervised/unsupervised/semi-supervised learning. Exposure to the semiconductor industry is a plus but not a requirement.
Ability to extract data from different databases via SQL and other query languages and applying data cleansing, outlier identification, and missing data techniques.
Strong software development skills.
Strong verbal and written communication skills.
Experience with machine learning and other advanced analytical methods
Fluency in Python and/or R
Experience with Tensorflow, and/or other statistical software including scripting capability for automating analyses
Experience working with time-series data, images, semi-supervised learning, and data with frequently changing distributions is a plus
Experience working with Manufacturing Execution Systems (MES) is a plus
Existing papers from CVPR, NIPS, ICML, KDD, and other key conferences are plus, but this is not a research position