• Developing programs to perform ETL of big data from professional esports games including a Python program that tracks 300+ players to allow for streamlined scouting.• Employing APIs, process memory reading, and computer vision to feature engineer 30 new statistics requested by analytic stakeholders.• Utilizing Python and SQL to create a 50,000+ entry dataset to create a neural network using pytorch that makes live-updating predictions of the outcome of a game.• Leading a team of 7 to develop, market, and sell our products to 5 different companies in the esports industry, achieving revenue of over $15,000.

• Developing an LSTM model to evaluate 1,000 professional players’ performances, aiding in scouting and identifying key areas for team improvement, ultimately enhancing the team’s competitive edge.• Collaborating with partnerships to assess the value of different locations of a sponsor’s logo on our jersey bytraining a custom image detection model (YOLOv5) with PyTorch to measure logo viewership, and implementpost-processing to filter out false positives – resulting in cost savings of $75,000 annually.• Designing and implementing an automated data acquisition and storage framework using AWS Lambda, S3, and EC2, creating ETL pipelines that collect and store over 1 TB of data from 4 APIs in a PostgreSQL database,reducing coaching staff’s manual data extraction by 10 hours a week.• Creating a responsive data visualization tool for our database using ReactJS, enabling stakeholders to intuitively explore and analyze over 1TB of data.