Jeehyun Lee

Machine Learning Engineer at 11street
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Contact Information
us****@****om
(386) 825-5501
Location
Gyeonggi, South Korea, KR

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Bio

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Credentials

  • DeepLearning.AI TensorFlow Developer Specialization
    Coursera
    Nov, 2021
    - Nov, 2024
  • Deep Learning Specialization
    Coursera
    Oct, 2021
    - Nov, 2024
  • Google Developers Machine Learning Bootcamp
    Google Developers
    Aug, 2021
    - Nov, 2024
  • ADsP (Advanced Data Analytics Semi-Professional)
    한국데이터산업진흥원
    Sep, 2020
    - Nov, 2024
  • TensorFlow Developer Certificate
    구글
    Nov, 2021
    - Nov, 2024

Experience

    • South Korea
    • Technology, Information and Internet
    • 100 - 200 Employee
    • Machine Learning Engineer
      • Feb 2022 - Present

      Responsible for Machine Intelligence Team Recommendations 1. Development and advancement of Amazon product personalized recommendation model (2022. 02 ~) - Developed 11st’s first personalized product recommendation model and launched service → CTR 56%, sCTR 51%, sCVR 57%, transaction amount increased by 75% compared to popular products by category 2. Retail box recommendation model development (2023. 05 ~ ) - When an order for 11Street retail products comes in, workers have previously judged and selected a box suitable for the ordered product among boxes of various sizes, but reduced work time and cost by developing a model that recommends an optimized box number - Currently applied to 20 11Street distribution centers 3. Advancement of repeat purchase recommendation model (2022. 06 ~ 12) - Conducting experiments and applying services to identify and improve model problems - Legacy code improvement → 46~56% reduction compared to the existing deployment time Show less

  • POSTECH Institute of Artificial Intelligence
    • Pohang-si, Gyeongsangbuk-do, Republic of Korea
    • Researcher
      • Sep 2020 - Nov 2020

      Face Recognition (1:1, 1:N) model development and optimization → Compared to the previous model, performance improved from 98.1% to 99.37%, and speed improved from 1.2 seconds to 0.2 seconds Face Recognition (1:1, 1:N) model development and optimization → Compared to the previous model, performance improved from 98.1% to 99.37%, and speed improved from 1.2 seconds to 0.2 seconds

Education

  • Seoul Women's University
    Bachelor, Mathematics
    2017 - 2022
  • Seoul Women's University
    Bachelor, Data Science
    2017 - 2022

Community

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