Geonhwan Ju (Leo, 주건환)
Experienced data scientist with a proven track record of delivering impactful data-driven solutions for business success across various industries including e-commerce, finance, and healthcare.
EXPERIENCE
Coupang — Staff Data Scientist
Mar 2022 - Present, Seoul
- Established and led a data science/analytics team dedicated to Rocket Growth (fulfillment service by Coupang).
- Optimized the pricing policy and redesigned Coupang’s seller fee structure for optimal performance.
- Developed KPIs and designed dashboards across the entire e-commerce business domain.
Unknot — Principal Data Scientist
Sep 2021 - Feb 2022, Seoul
- Developed deep learning-based models for trading in commodity futures and cryptocurrency markets.
- Constructed a robust real-time monitoring system for dynamic market environment.
Zalando — Data Scientist
Oct 2019 - Aug 2021, Berlin
- Optimized profitability of competitive pricing and increased the annual profit by 4 million euros.
- Measured the impact of competitors’ pricing strategy and developed a deep-learning model for sales forecasting.
- Developed an ML pipeline to match products from competitors’ websites with Zalando products.
Lunit — Research Scientist
May 2016 - Aug 2017, Seoul
- Developed CNN models for chest x-ray image analysis, surpassing the performance of experienced radiologists and improving radiologists’ performance as a second reader.
NH Investment & Securities — Quantitative Analyst
Oct 2015 - May 2016, Seoul
- Developed pricing models using Monte Carlo simulation, while also overseeing financial risk management and conducting scenario analyses.
EDUCATION
KAIST — Ph.D.
Industrial & Systems Engineering, Aug 2019
- Studied the dynamics of high-frequency markets by applying deep neural networks and reinforcement learning techniques.
KAIST — B.S.
Industrial & Systems Engineering, Aug 2010
- Major GPA: 4.12 / 4.3
PUBLICATIONS
- High-Frequency Trading Behavior Analysis using Inverse Reinforcement Learning, Working paper
- Learning Multi-market Microstructure from Order Book Data, Quantitative Finance, 2019
- Deep Learning-Based Automatic Detection Algorithm for the Detection of Malignant Pulmonary Nodules on Chest Radiographs, RSNA, 2017
- 3D Freehand Ultrasound Reconstruction using a Piecewise-Smooth Markov Random Field, Computer Vision and Image Understanding, 2015
