- Responsibilities include, but not limited to:
- 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 required. Exposure to the semiconductor industry is required
- 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 or desire to learn:
- Machine learning and other advanced analytical methods
- Fluency 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
- Experience working with time-series data, images, semi-supervised learning, and data with frequently changing distributions
- Experience working with Manufacturing Execution Systems (MES
- Existing papers from CVPR, NIPS, ICML, KDD, and other key conferences are plus, but this is not a research position
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 enhancement. 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.
