Machine Learning Modeling Engineer at Corning
Corning, New York, United States
🇺🇸 (Posted Jul 4 2018)
About the company
Corning is one of the world's leading innovators in materials science, with a 167-year track record of life-changing inventions. Corning applies its unparalleled expertise in glass science, ceramics science, and optical physics, along with its deep manufacturing and engineering capabilities, to develop category-defining products that transform industries and enhance people's lives. Corning succeeds through sustained investment in RD&E, a unique combination of material and process innovation, and deep, trust-based relationships with customers who are global leaders in their industries.
Corning is one of the world’s leading innovators in materials science. For more than 160 years, Corning has applied its unparalleled expertise in specialty 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.
Scope of Position:
Subject Matter Expert providing data analytics and machine learning modeling software development support to create novel computational methods and produce analytical models from available research & manufacturing data and to enable the company to effectively innovate and to enhance competitive advantage of Corning products.
Day to Day Responsibilities:
Work independently or as part of the team on data preparation, machine learning software development & modifications, model development, model testing and to allow models to be used by internal customers.
Interpret data analysis results in RD&E or business context, and articulate the implications of the results to the business function.
Write and maintain relevant support documentation.
Prepare and deliver relevant user training.
Skills & requirements
MS or PhD in computer science, mathematics or related discipline with specialization in data analytics & machine learning.
Required Years and Area of Experience:
1+ year of work experience in similar field, analytical software development or modeling experience and positive customer interactions.
Strong mathematical and programming skills and capability for independently preparing data (ETL functions of cleaning, consolidating, transforming data) for machine learning purposes.
Working knowledge of programming in Python.
Working knowledge of mathematical and statistical computing programming languages (MATLAB, R).
Knowledge of any other programming languages for scientific computing in Windows and Linux environments (such as C/C++, Fortran).
A foundation in software design principles and an ability to design and create code as necessary.
Comfortable working with Linux and Windows parallel processing clusters.
Deep knowledge of artificial neural networks with experience building accurate time series & sequential event models with recurrent neural networks and/or reinforcement learning algorithms.
Ability to communicate with and understand the complex requirements of scientists, engineers and professional staff in the development and deployment solutions.
Knowledge of machine learning libraries (such as Theano, Tensor Flow, Keras).
Knowledge of data mining suites (such as Weka or RapidMiner).
Background in Natural Language Processing.
Experience compiling and running codes on high-performance computers.
Knowledge of MPI, OpenMP, or other parallel/distributed computing paradigms.
Ability to analyze, optimize and debug scientific codes.
Experience working with cloud-based services such as AWS and Azure.
Strong interpersonal and communication skills and ability to work as a team player or independently are required.
Must be a proactive and solution-oriented problem solver.
Ability to undertake multi-projects.
Ability to bridge gaps between “domain” language (engineering, science) and “computing solution” language.
Proven ability to embrace and drive change.
Clear dedication to excellence and advancing beyond the current state.
Strong personal motivation.
Instructions how to apply
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