I am an alumni from the University of Georgia with a strong foundation in statistics and data analysis. I enjoy using statistical methods to solve real-world problems and turn complex data into meaningful insights that support business decisions. Throughout my projects, I have addressed multi-disciplinary questions: modeling 35 years of weekly rain-gauge chemistry data for the U.S. Forest Service, capturing multiplicative health risk factor interactions to optimize insurance pricing, and segmenting commercial shoppers using clustering algorithms to target campaigns.
Analyzed 35 years of weekly rain-gauge chemistry records from the U.S. Forest Service to model atmospheric wet deposition trends and statistically assess the environmental impact of the 1990 Clean Air Act.
Developed a predictive multiple linear regression pricing model using patient health habits (BMI, smoking) and demographics to identify exponential risk compounding and optimize premium pricing.
Constructed a consumer segmentation engine of 200 mall shoppers using K-Means clustering. Defined 5 key consumer personas and designed tailored marketing strategies for each segment.
Developed and designed a hosted R Shiny dashboard with responsive controls and reactive visualization modules to explore streaming popularity and acoustic features.
Fitted an Error, Trend, and Seasonality (ETS) state-space forecasting model to historical complaint data to forecast seasonal service bottlenecks and optimize airport ground operations staffing.
Developed interactive Excel dashboards using Pivot Tables, Power Query, and advanced charting to track, analyze, and visualize employee profits, product sales performance, and global bike sales metrics.