YOUNGJUN WOO

DATA SCIENTIST

Ann Arbor, MI• 734-***-****

adxco6@r.postjobfree.com

Website Github Linkedin

WORK EXPERIENCE

NFTBank -NFT Portfolio Tracking & Analytics Platform Aug 2020 – Feb 2021 Data Scientist

● Key member of the Data Science team pioneering solutions to break new ground for the bank in the rapidly growing NFT market

Built a feature engineering pipeline that delivered 1k+ features for each of 14 DApps to train machine learning models for NFTs valuation, resulting in a 15% average MAPE

Developed another pipeline export transactional data into a tax report format, resolving a key challenge for NFT traders

The strong showing resulted in securing $1.4M in seed funding as a strength of the team’s vision for ways to capitalize on dynamic opportunities within the NFT landscape

● Leveraged a variety of key communication resources to build powerful relations and secure buy-in for the solutions from NFT whale investors

Used Google BigQuery to extract financial indicators from 3TB+ of NFTs transaction data, providing 1k+ users with key insights into NFT market trends and weekly NFTs trades summaries by personalized weekly email newsletters

Crafted expert posts educating users on best practices for utilizing NFTBank’s features to improve trading capabilities and performance

Integrally involved in rapidly growing the user base to 1k+, trading $10M+ in NFTs volume to position the group as a major player in the industry

DATA SCIENCE PROJECTS

European Soccer Match Prediction Jan 2023 – Present

● Implemented a match results classification model by analyzing 299 club teams, 11,060 players, and 25,979 matches from Europe’s top 11 leagues, improving accuracy by 18%

● Plan to establish a dashboard informing match predictions with variables based on the feature importance from the trained Random Forest model

Sepsis Prediction Data Challenge Jan 2023 – May 2023

● Trained a binary classification ensemble model consisting of the LightGBM, Random Forest, and XGBoost to predict sepsis from 21,634 patients with 40 vital signs, laboratory values, and demographic variables, leading to 5% higher AUC and 30% lower BER than medians of other participants Tennis Club Database and Dashboard Aug 2022 – Jan 2023

● Enhanced members’ accessibility to matches and ranking data by building a dashboard using the Google Looker Studio, resulting in the increased number of club members from 8 to 36

● Developed a data pipeline for the club database, leading to 83% time savings on updating the database EDUCATION

University of Michigan Ann Arbor, MI Aug 2021 - Apr 2023 M.S. Data Science (GPA: 3.6 / 4.0)

Yonsei University Seoul, Korea Mar 2013 - Aug 2020 B.A. Double Major in Statistics and Economics (GPA: 4.0 / 4.5) B.S. Minor in Mathematics University of California, Berkeley Berkeley, CA Jan 2019 - Jun 2019 Undergraduate exchange program, focusing on Mathematics (GPA: 3.85 / 4.0) CORE STRENGTHS

Machine Learning Regression Analysis Dimension Reduction Analysis Survival Analysis Time Series Analysis Bayesian Statistics Deep Learning Computer Vision Cross-Functional Collaboration Dashboard Development TECHNICAL SKILLS

Python R SQL C++ Julia JavaScript