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PhD in Mathematics, Statistics, Machine Learning, Engineering or related field
Strong communication skills with a demonstrable ability to lead multiple teams/projects in parallel, reporting to senior executives
Demonstrated analytical problem-solving skills with ability to troubleshoot complex problems and address root causes.
Strategically focused, impact oriented, highly organized and adaptable.
Preferred work experience in data science solution implementation
Knowledge in relational databases design and SQL
Strong data validation skills
Experience working with streaming data
Work with data engineers, software engineers, process engineers, equipment engineers, NPI engineers, Test engineers, etc. in a collaborative manner to develop new data science solutions that improve quality, improve yield, reduce deviations, improve manufacturing cycle time, reduce cost, extend manufacturing capabilities, etc.
Draw from a broad background of data-mining techniques in mathematics, statistics, information technology, machine learning, data engineering, design of experiments (DOE), and visualization to discover insightful patterns in semiconductor manufacturing data and identify patterns in data from which predictive models, actionable insights, and solutions that able to develop.
Work on projects and develop solutions that would be of high impact to various areas at assembly process.
Work closely with IT, Data Analytics Organization and Big Data Management on data management, taking an idea, access and pre pare necessary data, work with data scientists to create machine-learning models, develop to an application with intuitive user interface, integrate with any pre-existing systems, and demonstrate successful use cases and wins.
Developing new or enhancing prior data acquisition and Extract, Transform and Load pipelines from various sources into big data ecosystem.
Working with various scientific data such as equipment sensor data and machine logs, image and various types of signals, manufacturing process data to extract meaningful information for analytics.
Creating intuitive user interface for interactive data visualization to explain insights from data.
Identify and understand the data sources that are relevant to the business problem.
Optimize table schema based on usage patterns. Actively participate in growing and develop the team of data engineers.
Ensure that the data is fit to use for data science applications
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