Sean (Sihyun) Kwon

Data Scientist

New York, NY

213-***-****

adb0gv@r.postjobfree.com

github.com/seankwon1014

linkedin.com/in/seankwon1014

Columbia University, MS in Applied Analytics, GPA 3.92/4.0 Dec 2019, New York, NY

• Focus Areas: Machine Learning Theory with Statistics, Gradient Boosting, Deep Learning, Fraud Detection

• Core courses: Anomaly Detection, Capstone Project, SQL, Managing Data & Relational DB, Frameworks & Methods for ML Hanyang University, BS in Sociology, GPA 3.75/4.0 Feb 2008, South Korea

• Summa Cum Laude; Focused on social statistics to study social issues Education

Capstone Project - Data Science Team, Spruce Jun 2019 - Aug 2019, New York, NY

• Provided a foreclosure detection model by using Deep Learning with SMOTE; increased ‘recall’ score from approximately 20% of logistic regression to 72% of Deep Learning-FastAI Kaggle Competition

• Santander Customer Transaction Prediction: Top 11%, Binary Classification, Python, XGBoost & LightGBM

• Elo Merchant Category Recommendation: Top 11%, Regression, Python, XGBoost & LightGBM Projects

Summary

• Data Scientist with experience executing data-driven solution using deep learning, gradient boosting, and clustering, creating efficiency within marketing, product & service strategy, and anomaly detection

• Data Analyst with 5 years of experience analyzing smartphone-related online transaction data Skills

Machine Learning

Anomaly Detection: PyOD (DBScan, Meanshift, Autoencoder) Deep Learning: Pytorch / FastAI Gradient Boosting: XGBoost, LightGBM Regression / Clustering / Decesion Tree / Recommender System / NLP Data Visualization: Tableau/Matplotlib/Seaborn

Python: Numpy / Pandas / Sklearn / H2O

SQL / Hadoop

AWS: EC2 / Sage Maker R

Columbia University

Machine Learning Tutor Jul 2019 - Dec 2019, New York, NY

• Taught graduate students Python and SQL one-on-one at the School of Professional Studies Samsung Electronics Global HQ

Data Scientist Mar 2016 - Jun 2018, South Korea

• Marketing: Defined target customers based on ‘gain and lift chart’ by XGBoost; provided personalized content recommen- dation by collaborative filtering; improved download rates from 3% to 9% in the Galaxy Wallpaper Store

• Product Strategy: Created user clusters for Bixby, A.I assistant in Galaxy through K-means; provided insights for customer churn by delivering main used features and WAU by a cluster and period; decreased churn rate from approximately 45% to 30% in the next version

• UX: Provided UX pain points by detecting where users frequently lose their ways in the setting menu by analyzing UX log data on SQL; short-cut menu related to the analysis was added in the subsequent version Data Analyst Aug 2010 - Feb 2016, South Korea

• Analyzed sales trends by app categories and payment methods in the Galaxy Apps Store; contributed to adopting new pay- ment methods based on expected profits

Professional Experience

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