Data Science Intern
Current- Developed models to determine optimal pitch sequences for pitchers in the MLB by leveraging clustering techniques, Recurrent Neural Networks (RNNs), and game theory principles.- Designed and implemented a web-scraping tool using Selenium to extract player salary and contract data across multiple soccer leagues and seasons, automating data collection for analysis and ensuring scalability for future updates.- Performed data analysis to ensure completeness and accuracy of data across different soccer matches and leagues, identifying gaps and inconsistencies to improve model reliability. - Processed game event data into actionable statistics used for modeling, including aggregating player performance metrics and calculating advanced statistics