Developed a Bayesian hierarchical model to predict individual user value for each advertiser, enhancing real-time bidding strategies and driving increased return on ad spend and revenue per clickManaged a cohort of three data scientists, guiding them in developing models to generate automated insights on advertising campaign performanceCreated a model to provide advertisers with bid recommendations, including projected impressions, clicks, spend, and revenue, enabling data-driven decisions for optimal bidding strategies

Derived consumer insights from terabytes of purchase data and visitation dataDeveloped model of user behavior to discover thousands of restaurant's likely competitors and repeat visitor visitation patternsCreated deep learning social media behavior model on visitation and purchase data to segment consumers

Training deep learning models on billions of rows of dataImproving computation time and accuracy of existing collaborative filtering modelsDeveloping product concepts for expansion into new markets

Forecasting demand trends to recommend parts and component purchasesAnalyzing upholstery sales records Writing over $700,000 in purchase orders

Supervised over 60 children between the ages of 2 and 14Created or assisted with daily activities for the childrenDeveloped non-verbal communication skills