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 Smart Manufacturing 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 expert Data Scientists, Data Engineers, Business Areas Engineers, and UX teams for data analysis projects and improvement of existing tools. There will be significant opportunities to perform exploratory and new solution development activities. Join us today to embark your career opportunity!
Roles & Responsibilities
- Designs, develops and programs methods, processes, and systems to consolidate and analyze unstructured, diverse “big data” sources to generate actionable insights and solutions for client services and product improvement.
- Interacts with product and service teams to identify questions and issues for data analysis and experiments.
- Develops and codes software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources.
- Identifies meaningful insights from large data and metadata sources; interprets and communicates insights and findings from analysis and experiments to product, service, and business managers.
- 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.
Requirements
- 2+ years of experience with the ability to extract data from different databases via SQL and other query languages and applying data cleansing, outlier identification, and missing data techniques.
- Strong verbal and written communication skills.
- Machine learning and other sophisticated analytical methods
- Proficiency in Python and/or R
- pySpark and/or SparkR and/or SparklyR
- Hadoop (Hive, Spark, HBase)
- Teradata and/or another SQL databases
- Tensorflow, and/or other statistical software including scripting capability for automating analyses
- SSIS, ETL
- Javascript, AngularJS 2.0, Tableau
