Client: Fortune 150 CPG Company – Promotion Campaign Effectiveness Automation (Best Technical Solution Award)• Developed end-to-end automation pipeline from data gathering to model building and visualization, achieved product scalability with minimal manual intervention and significant reduction in development time• Implemented Discrete Choice Model in R using 3 years Nielsen data to analyze promotion effectiveness• Designed interactive dashboard in Tableau to visualize modeling results by various retailer, brand, and promotion combinations, presented insights and actionable recommendations to category directorClient: Mall of America – Guest Experience Improvement & Ridership Forecast (Case Competition Finalist)• Merged and transformed 17M rows of transaction data in SQL, applied K-Prototype clustering algorithm to develop segment personas based on similar behaviors and characteristics• Delivered tailored recommendations with ridership bundles and demand redistribution to optimize traffic flows in R and Tableau• Built ensemble predictive model using Random Forest in R to forecast daily ridership for upcoming year, improved daily staffing accuracy by 20% and reduced the error in annual revenue forecast by $1.6MClient: PwC – Loss Ratio Reduction Analysis (Case Competition Finalist)• Led a 6-person team to analyze an auto book of business through clustering and exploratory data techniques in R and Tableau to discover insights to drive down the loss ratio• Quantified findings to generate actionable fraud-detection recommendations, leading to an increase in customer understanding and methods to avoid potential fraud by 5%

• Synthesized 80K financial raw data in Excel for 12 ASEAN entities on a daily basis and delivered user friendly report with expedited data-gathering process• Analyzed financial performance and KPIs, presented monthly and quarterly results to cross-functional executive management teams, supported fact-based decision making and better financial strategic planning