Hang Yang Email & Phone Number

New York, Ny, Us

New York, United States

- Solution Architecture

Seattle, Washington, United States

- Conducted Survival Analysis and Random Forest to categorize user profiles and predict customer tenure, successfully identifying the top three churn drivers and 13K potential churn customer in October- Developed and validated a business hypothesis through A/B testing, assessing impact on customer engagement strategies, successfully demonstrated a 12% decrease in customer churn rate- Optimized delivery delay forecasts using AWS SageMaker XGBoost with RFE feature selection, achieving 83% precision.- Implemented a priority shipping strategy for at-risk customers by using prediction model, contributing to an 8% improvement in retention rates- Utilized KNN clustering for accurate prediction of waiting times in fulfillment centers. Integrated with XGbregressor to develop an effective picking strategy, enhancing unit picking throughput by 2.7%, average time saving of 14s per order pick- Collaborated across teams to establish an initial data quality check pipeline with Great Expectations to support developing MLOPS

New York, United States

- Developed Ridge and Lasso Regression to predict variables akin to hospital stay durations, focusing on enhancing predictive accuracy while preventing overfitting. Demonstrated model efficiency with an R square of 83%- Created over 10 Tableau dashboards, translating published medical studies into visually intuitive charts effectively selecting relevant patient/study groups, enhancing decision-making processes based on data-driven insights- Conducted PCA to reduce the dimension from 44 original features to 5 principal components. Subsequently applied the K-means algorithm to segment a large dataset with over 70,000 entries into 4 clusters

Boston, Massachusetts, United States

- Utilized Google Analytics to retrieve and analyze user behavior data, contributed over 800 lines of SQL query in Snowflake; Build an ecommerce conversion funnel with Tableau, successfully pinpointing key drop-off points for in-depth analysis- Employed Tableau to ascertain customer category interest levels, identified the top categories for transactional customers, and developed 3 targeted marketing strategies based on data insights- Formulated targeted marketing strategies based on transactional customer top existing page, significantly addressing checkout page abandonment and enhancing order conversion rates.- Implemented an XGBoost model to identify new customers unlikely to place orders. Enabled pinpointing user characteristics linked to inactivity, early identification of 8K at-risk customers, informed subsequent targeted engagement strategies

Shanghai, China

- Employe Power Query and Pandas package in Python to perform data cleaning, visualized the results by using matplotlib packages, and converted over 600,000+ entrie of data to be more readable, and user-friendly- Scraped data from 100+ competitor account by using BeautifulSoupand Selenium to monitor competitor behavior, conducted Python TF-IDF analysis generated Word Frequencies table and increased the competitor behavior prediction rate by 70%- Utilized Python to design a competitor monitoring system, which automatically scraped data from multiple competitor accounts(by day), and optimized the procedure by 90%

Shanghai, China

- Generated dozens of user portraits under phone branding topics based on User Behavior Analytics and presented the result to senior leadership, providing a comprehensive summary of marketing strategies and key competitors- Conducted Weibo data platform research by gathering useful data to develop an understanding of customer behavior, demographics, and life cycle of a local brand in China; presented data that helped guide decisions of that company, thereby raising its popularity 43%

Shanghai, China

- Leveraged Power BI to analyze inventory turnover rates in multiple cities, leading to data-driven sales strategy enhancements- Conducted Web Scraping of PepsiCo's online store data to identify key factors influencing customer satisfaction. Created product satisfaction level categories using word cloud visualizations, guiding targeted product improvement efforts- Implemented Power BI incorporating historical sales data and market trends. Improved inventory planning and waste reduction, enhancing supply chain efficiency and aligning production with market demands

Master Of Science - Ms, Data Science, 3.7

Bachelor Of Science - Bs, Logistics, Materials, And Supply Chain Management, 4.0

Bachelor Of Science - Bs, Business Data Analytics, 4.0