Data Scientist, Global Operations Intelligence, SMAI

Our vision is to transform how the world uses information to enrich life for all.

Join an inclusive team passionate about one thing: using their expertise in the relentless pursuit of innovation for customers and partners. The solutions we build help make everything from virtual reality experiences to breakthroughs in neural networks possible. We do it all while committing to integrity, sustainability, and giving back to our communities. Because doing so can fuel the very innovation we are pursuing.

As a Data Scientist 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.

Responsibilities include, but are not limited to:

• Demonstrate a strong desire to build a career as a Data Scientist in highly automated industrial manufacturing, focusing on analysis and machine learning with large, diverse datasets (terabytes to petabytes).
• Apply expertise in statistical modeling, feature extraction and analysis, and supervised, unsupervised, or semi-supervised learning. Experience in the semiconductor industry is a plus, but not required.
• Extract data from various databases using SQL and other query languages, and perform data cleansing, outlier detection, and missing data handling.
• Exhibit strong software development skills.
• Communicate effectively, both verbally and in writing.
• Possess experience with, or willingness to learn:
  • Machine learning and advanced analytical methods
  • Python and/or R
  • pySpark, SparkR, or SparklyR
  • Hadoop ecosystem (Hive, Spark, HBase)
  • Teradata or other SQL databases
  • TensorFlow and other statistical software, including scripting for automation
  • SSIS, ETL processes
  • Javascript, AngularJS 2.0, Tableau
• Experience working with time-series data, images, semi-supervised learning, and data with frequently changing distributions is a plus.
• Experience with Manufacturing Execution Systems (MES) is a plus.
• Having published papers in conferences such as CVPR, NIPS, ICML, KDD, etc. is a plus, though this is not a research position.