− Used Python to build and deploy ML models that scored transactions based on their likelihood of being fraudulent, leading tothe prevention of over $300,000 in fraud losses over a span of 6 months− Conducted performance analysis to evaluate the need for model replacement and completed retraining on underperformingmodels, resulting in monthly savings of $4,000− Evaluated the efficacy of third-party vendor data to assist in determining domain budget allocation, then implemented theexternal data into the fraud policy, saving $600,000 in potential fraud losses− Identified fraud patterns by running queries, creating visualizations, and dashboarding to produce fraud policy changes thatprotect the company from fraudulent customers− Collaborated with legal and compliance teams to explain model architecture and variable importances in a non-technicalmanner to ensure fair lending practices

− Worked 1-on-1 with a client to deliver a machine learning solution to help identify potential hurricanes using AWS services− Delivered presentations to clients and team members detailing machine learning capabilities on AWS− Gained experience working with cloud solution strategies and obtained AWS Certified Cloud Practitioner certification

− Designed machine learning model to identify potential cases of human trafficking− Wrote data scraping scripts to gather and store data in computable formats− Delivered reports and public presentations documenting the results of various projects

− Instructed students on implementing various data structures in Python− Assisted students in analyzing and debugging Python code− Held office hours to provide students with additional feedback and aide

− Aided Professors in grading and critiquing undergraduate students’ coursework− Computed scores and analyzed data on Excel spreadsheets using macros and formulas