About The Position

Glen Raven is seeking a technically strong summer intern to design and implement a machine-learning model that predicts fabric performance in the PPE Sector using historical lab data. This role is project-driven and hands-on, with responsibility spanning data exploration, feature engineering, model development, and evaluation.

Requirements

  • Pursuing a degree in Computer Science, Data Science, Engineering, or a related quantitative field
  • Strong proficiency in Python and common data science libraries (pandas, NumPy, scikit-learn, etc.)
  • Solid understanding of machine learning fundamentals, statistics, and model evaluation
  • Experience working with structured datasets and version-controlled codebases (Git)
  • Ability to work independently on an open-ended technical problem

Responsibilities

  • Build end-to-end machine learning pipelines to predict fabric performance metrics from historical testing data
  • Perform data cleaning, feature engineering, and exploratory analysis on large, structured datasets housed in a data lake
  • Evaluate and compare modeling approaches (e.g., regression, tree-based models, ensemble methods, simulation-informed models)
  • Validate model performance using appropriate metrics and cross-validation techniques
  • Collaborate with Product Development and Data Analytics partners to ensure scalable and reproducible workflows
  • Document model architecture, assumptions, and limitations for future extension
  • Deliver a final technical readout and working prototype at the conclusion of the internship
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