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Bio

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Daniel Byrne is a seasoned data scientist with expertise in predictive modeling and visualization. At Sovendus, he developed and tested models utilizing PyTorch, pandas, numpy, and SQL to forecast client spend on campaigns, and created interactive visualizations to communicate results to sales teams and customers. Byrne holds a Bachelor of Science in Engineering from the University of Connecticut, where he also excelled in the Cybersecurity Club and Mathematics Club, with a focus on Theory & Algorithms and Lambda Calculus.

Experience

  • Sovendus
    • Karlsruhe, Baden-Württemberg, Germany
    • Data Science Intern
      • Mar 2022 - Sep 2022
      • Karlsruhe, Baden-Württemberg, Germany

      Developed & tested models to predict affect of client spend on campaigns with the company using tools such as pytorch, pandas, numpy, python, SQL & matplotlib. Also developed visualizations in communication with the sales team to communicate those results to customers.

Education

  • 2017 - 2022
    University of Connecticut
    Bachelosr of Science in Engineering, Computer Science and Engineering
  • 2021 - 2022
    Karlsruhe Institute of Technology (KIT)
    Austauschprogram, Informatics
  • 2017 - 2022
    University of Connecticut
    Bachelors, Matematics
  • 2017 - 2022
    University of Connecticut
    Bachelor of Arts - BA, German Studies

Suggested Services

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Industry Focus. “Computer Software”

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