Performed price optimization to maximize sales and take-rate through in-depth analysis of the current status and problems of the commission systemAnalyzed customers’ app usage behavior to set company’s KPIs with company executives; contributed to task establishment and prioritizationDeveloped dashboards to measure performance of app’s current status and changes; continuously suggested possible improvements to main business stakeholders based on dataConducted more precise analysis on AB tests using statistical approach and provided necessary metrics and data driven insights to stakeholders through persuasive verbal and written communicationsMaximized advertisement profits by developing ctr prediction model and analyzing factors affecting demands of advertisements Conducted fraudulent transaction analysis and extracted useful patterns to detect fraudulent use of credit cardsImplemented recommendation algorithm based on results of product usage analysis, reflecting needs of customers

 Developed ensemble sales prediction model, using machine learning, deep learning, and time series forecasting techniques; optimized strategic marketing planning by automating prediction system Performed natural language processing by developing deep learning algorithm using Pytorch to utilize unstructured data, such as text data, as model’s features Designed multiclass classification model to optimize customer service system and improved accuracy by 10%; increased business utilization of the model through cross-functional communications with other teams Conducted exploratory data analysis to formulate and test hypotheses for feature extraction and feature engineering; created curated datasets using SQL queries  Built machine learning pipelines that streamline exploratory data analysis, predictive modeling, and machine learning Communicated with other data scientists to generate ideas about models and applied to problem solving; performed state-of-the-art technology research and shared with teammates

 Developed collaborative filtering (CF) and logistic regression to predict customer preferences and enhance customer satisfaction; implemented algorithms from scratch using Python Productionized optimal recommender system through streamlined evaluation metrics for various algorithms and increased precision by 25% Conducted data cleaning and preprocessing using SQL and Python pandas; performed cross-functional communications with other teams to determine criteria for feature engineering Wrote weekly report on data analysis using advanced querying, visualization, and analytics tools, developing marketing strategies  Created jupyter notebook templates that streamline exploratory data analysis, predictive modeling, and machine learning

 Performed time series analysis and developed linear mixed-effect model to explain covariance between subjects’ neuroticism and mood score

 Analyzed financial data of client’s manufacturing subsidiary to propose strategic transition, resulting in estimated annual savings of $0.4M and 10% increase in market share Conducted primary and secondary research on agricultural industry to determine external market drivers and forecasted growth Researched global seed market to identify potential M&A targets that fit both the company’s business portfolio and strategic objectives Assessed key drivers in distribution structure, marketing strategy, and capital structure of the world’s leading seed companies to conduct benchmarking analysis

 Conducted research on how Social Contribution affects images and profits of corporations; translated articles and thesis papers into Korean to provide resources for team members Wrote weekly reports on social contribution activities implemented by different corporations and presented reports to team members