About me

My interest lies in areas that are closely tied to our daily lives, where technological advancements can significantly reshape our experiences. This is why I have worked in both the gaming and e-commerce industries, sectors where people can feel the innovation directly. What drives me is the challenge of solving problems and improving the current state of things. I believe that the most novel solutions often come from combining concepts across different fields, which is why my experience across various industries will help me excel. My goal is to continue collecting, connecting, and changing just as I did as a data scientist, but now as a researcher. It’s more than just a change in title; it’s an evolution in how I contribute to shaping the future.

What i'm doing

  • mobile app icon

    Game Analytics

    Analyze player experiences to boost engagement and retention.

  • Web development icon

    Sales & Market Analysis

    Analyze sales data and market trends to drive strategic business decisions.

  • design icon

    LLM

    Utilized LLMs for social network and game community analysis.

  • Web development icon

    AI & Python Development

    Building AI solutions with Python to tackle complex challenges.

Resume

Experience

  1. Krafton

    Data Scientist | 2021 — Present

    - Developed a retention simulator for PUBG: NEW STATE based on the Leslie matrix to predict Monthly Active Users (MAU) by modeling inflow and retention. Deployed the simulator as a web application, allowing marketing team members to set goals and budgets for user acquisition (UA) campaigns.
    - Created a sales prediction model using Prophet to forecast sales in the Russian market, addressing challenges posed by the Russian-Ukraine war and app store payment restrictions. The model identified Russia as a high-potential market (ranked within the top 5 sales regions). Based on these insights, the team decided to implement Xsolla, allowing for more effective payment solutions and sustaining revenue despite external constraints.
    - Built a churn prediction model using XGBoost, significantly improving model accuracy from 78% to 87% through feature set refinement. The model's outputs were integrated into Braze, a marketing tool, to target churn-prone users with personalized retention strategies. Managed the entire machine learning lifecycle using MLflow.
    - Designed and implemented an ETL module using Python and DBT (Data Build Tool) principles to streamline data transformation processes and improve pipeline efficiency. This module enhanced the efficiency and consistency of data workflows, improving overall data management across the platform.

  2. Market Kurly

    Data Scientist | 2019 — 2020

    - Developed data-driven category management strategies by analyzing sales efficiency using R and Python, improving the performance of the seafood category, which led to a 28% sales increase compared to other fresh categories. By identifying underperforming areas, the strategies enhanced both product assortment and category profitability.
    - Created a weekly trend report using Python, Elasticsearch, and data from internal and external sources (e.g., Naver Shopping API) to identify emerging product trends. By introducing trend-driven items at the right time, this initiative helped increase sales by more than 2 times for relevant categories, optimizing product exposure and enhancing overall sales performance.

  3. ETRI (Electronics and Telecommunications Research Institute)

    Intern | Dec 2017 – Feb 2018

    - Utilized Google ImageNet data to classify pose categories, contributing to the definition and refinement of pose classifications.
    - Worked with CCTV data for vehicle detection, responsible for marking bounding boxes around cars as part of the data labeling process.

Education

  1. Korea University (GPA: 4.0/4.5)

    B.S. in Computer Science and Engineering | Class of 2021

    - Awards: Capstone Competition – Korea University (3rd Place Winner): Project Theme: "Stock Trader Using DQN (Deep Q-Network)"
    - Coursework: Deep Learning, Machine Learning, Introduction to Convex Optimization, Signals and Systems, Data Communications, Wireless Communications, Computer Networks, Probability and Random Processes, Discrete Mathematics, Data Structures, Algorithms, Operating Systems, Digital Logic Design, Computer Architecture, Databases

My skills

  • Python
    80%
  • SQL
    90%
  • C
    60%
  • React
    50%
  • Airflow
    50%

Contact

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