Data Scientist

About Me

I’m a data scientist specializing in quantitative marketing, with expertise in causal inference and machine learning to optimize marketing and product strategies. Skilled in Python, SQL, and data visualization, I have a strong track record of using data-driven insights to drive engagement and conversion. I’m passionate about translating complex data into actionable strategies that enhance growth and efficiency. Resume is available.

Projects

• Machine learning model for binary classification (dataset: US car accidents)
  Developed a robust machine learning model for a large dataset of 2.8 million records, focusing on binary classification. Employed feature engineering and hyperparameter tuning to select the optimal model. Implemented SMOTE to effectively address class imbalance in the target variable. (PySpark)

• Monitoring plan design for gradient boosting model (dataset: Pima Indians diabetes)
  Designed a comprehensive monitoring plan for a gradient boosting model, including data drift detection, performance tracking, sensitivity analysis, and conceptual soundness checks. (Scikit-Learn, Scipy, Statsmodels)

• Metrics Development for Pokémon (dataset: Pokémon Stats)
  Developed new metrics to overcome the limit of “Total”, the evaluation metric included in the current dataset.

Education

• Ph.D.(ABD), Quantitative Marketing, Syracuse University (2024)
• M.A., Economics, Syracuse University (2023)
• M.Sc., Quantitative Marketing, Seoul National University (2017)
• B.S., Finance & Accounting, Yonsei University (2013)

Skills

• Languages & Databases: Python, R, SAS; SQL
• Analytical Modeling: A/B Test, Causal Inference, Time Series Analysis
• Marketing Modeling: Marketing Mix Modeling (MMM), Multi-Touch Attribution (MTA), Market Segmentation, Churn Prevention, Spender Detection, Pricing Bundling, Customer Lifetime Value (CLV), Lifetime Value (LTV)
• Machine Learning: Scikit-learn, PySpark, TensorFlow, PyTorch, Keras, Numpy, Pandas; Algorithm: Logistic & Multivariate Regression, Random Forest, Gradient Boosting, Support Vector Machine (SVM), Neural Network
• Data Visualization: Tableau, Matplotlib, Seaborn, Plotly, Ggplot2, Shiny
• IDE & Documentation: Google Colab, Jupyter Notebook, R Studio; LaTeX, Git

Experience

Doctoral Researcher / Instructor @ Syracuse University (Syracuse, NY) (Aug 2017 - Dec 2024)

• Utilized a zero-inflated Poisson model to suggest a policy design to enhance the profitability of the referral program by analyzing shifts in customers’ opportunistic behavior. (R)
• Led a SAS programming workshop to train graduate students to analyze Nielsen Consumer Panel Data and create customer insights. (SAS)
• Guided student teams through the successful completion of marketing strategy projects by assisting in defining marketing problems, conducting marketing environment analyses, and devising marketing strategies.

Data Analyst / Digital Marketing & Business Development Lead @ Kakao Entertainment (Seoul, Korea) (May 2013 - Feb 2015)

• Improved the ROI of the marketing campaigns by analyzing their effects from traffic, product, and revenue perspectives and providing data-driven decision support for marketing, product, and sales leaders
• Acquired new users and bolstered the revenue YoY by designing and implementing digital marketing campaigns such as social media advertisements, banner advertisements, and push notifications.
• Enhanced the conversion rate of push notification marketing by developing a customer segmentation system of the MarTech product based on purchase history as a project manager.
• Increased the MAU of the e-books & podcasts categories by finding, selecting, and marketing products and developing merchandising initiatives with vendors.

Research Assistant @ Korea Productivity Center (Seoul, Korea) (Aug 2012 - Dec 2012)

• Collected quantitative and qualitative customer data from surveys, focus group interviews, mystery shopping exercises, and interviews with employees, collaborating with marketing research agencies.
• Generated actionable customer insights from collected data, assisting consultants in devising strategic initiatives aimed at improving customer satisfaction and gaining a competitive advantage over industry competitors