Corning

Requisition Number: [[id]] 

Corning is one of the world’s leading innovators in materials science. For more than 160 years, Corning has applied its unparalleled expertise in speciality glass, ceramics, and optical physics to develop products that have created new industries and transformed people’s lives.

Corning succeeds through sustained investment in R&D, a unique combination of material and process innovation, and close collaboration with customers to solve tough technology challenges.

Corning's Manufacturing, Technology and Engineering division (MTE) is recognized as the leader in engineering excellence & innovative manufacturing technologies by providing diverse skills to Corning’s existing & emerging businesses.

We anticipate & provide timely, valued, leading edge manufacturing technologies and engineering expertise. We partner with Corning’s businesses and the Science & Technology division. Together we create and sustain Corning’s manufacturing as a differential advantage.

Purpose of the Position:

As a leading manufacturing and technology company, Corning manufacturing systems generate a vast amount of process and product data through advanced control and measurement systems. The data analytic engineer provides statistical concept and support different Corning business to implement data driven decision making and help them to improve their manufacturing processes or design new ones for our long-term success. At this position, he/she will be responsible for proposing solutions from data acquisition, data storage, data analysis and visualization. In parallel, the data analytics engineer will work with a global multifunctional team to advance Corning’s Smart Manufacturing strategy with the state-of-art technologies (such as advanced analytics, machine learnings, etc.).

Day to Day Responsibilities:

• Attend and manage business solution projects.
• Design and develop data analytics training curriculums to popularize basic data analysis capabilities.
• Define, design and implement appropriate solutions and options (ie. statistics methods or machine learning techniques) to perform exploratory and targeted data analysis. Apply the findings to quality, process, measurement and control solutions.
• Act as an expert in data analytics field to close communicate with team members to make sure the solution is properly integrated with other business solution if necessary
• Write technical reports summarizing development, validation and application of the technical analysis.
• Share and contribute Corning’s data analytics community of practice in developing the capabilities and promoting areas of applications.
• Lead the delivery of basic and advanced data analysis course in the organization and provide technical consultancy to wide spectrum of user groups within the company.
• Be responsible to understand problems from the customer's point of view and translate into data analytics solution and work plans. Communicate technical analysis and results to internal customers.
• Coach junior engineers in advancing data analytics capability as part of building the critical mass for the team.
• Establish and maintain external linkage with academic and industry partners to stay abreast in data driven technology for value-added deployment across Corning businesses.

Education & Experience

• Be well acquainted in dealing with mathematical & statistical algorithms (e.g. regression, Bayesian models and numerical optimization etc.) and machine learning techniques (e.g. clustering, classification and neural networks etc.)
• Familiar with manufacturing statistical application (DOE, MSA, SPC, DMAIC, etc)
• Working knowledge of programming in Python.
• Data Analysis knowledge using any mix of software including, but not limited to:JMP, MATLAB, R, Minitab, SAS.
• Ability to analyze, optimize and debug scientific code.
• Experience in work with complex databases (such as SQL server, PI, Hadoop, etc).
• Experience in building and maintaining BI dashboards (such as PowerBI, tableau, Dashbuilder, etc)
• Skilled with common workplace and database software (such as Pycharm, Jupyter notebook, Databricks, PI, etc)
• Ability to bridge gaps between “domain” language (engineering, science) and “computing solution” language.
• Ability to communicate with and understand the complex requirements of scientists, engineers and professional staff in the development and deployment solutions.
• Experience compiling and running code on high-performance computers.