Data Analytics Intern - Summer 2027

CorningTown of Erwin, NY
$42,510 - $82,810Onsite

About The Position

Corning is a global leader in materials science, specializing in glass, ceramics, and related materials. The Environmental Technologies segment focuses on manufacturing ceramic substrates and filter products for emissions control. This internship role is designed to support various projects, particularly in manufacturing technical problem-solving, with a focus on process data anomaly detection and dashboarding for engineers. The intern will also be expected to develop predictive tools to aid business and technical decisions and create graphical presentations to communicate results effectively. A key aspect of the role involves explaining model methods and predictions, including feature importance.

Requirements

  • Pursuing a degree in a data science or statistical field.
  • Must be available for 10 weeks during Summer 2027.
  • Graduation date of December 2027 or later.
  • Interested in working for Corning, Inc after graduation, if presented with a full-time job opportunity.
  • Willing to work at the posted job location.
  • This position does not support immigration sponsorship.

Nice To Haves

  • Familiar with open-source tools for data analytics such as Python, TensorFlow, PyTorch.
  • Familiarity with neural networks, such as CNN, and decision trees.
  • Familiarity with outlier identification methods such as Isolation Forests.
  • Familiarity with SQL and PowerBI.
  • Preference will be given to graduate students in a related data science field.
  • Experience with management/manipulation of large data sets.
  • Familiarity with machine learning techniques for supervised and unsupervised learning.

Responsibilities

  • Support a range of projects from technical problems in manufacturing.
  • Focus on process data anomaly detection.
  • Develop dashboarding to assist engineers.
  • Create predictive tools that will enable both business and technical decisions.
  • Devise graphical presentation methods to explain results to others.
  • Explain model methods and predictions in terms of feature importance.

Benefits

  • Competitive salaries
  • Assistance with housing and travel
  • Meaningful project and final project presentation
  • Social and networking opportunities
  • Weekly tech talks
  • Community involvement
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