• Retrained and modified machine learning models in Python to increase projected payment error estimates, improving overpayment prediction accuracy by about 10 percentage points.• Enhanced the efficacy of modeling datasets in SAS creating 3 additional variable groups, adding 6 additional samples, and constructing datasets larger than 6 million rows and hundreds of columns for more robust modeling results.• Demonstrated thought leadership by creating compelling posters and refining technical proposals presenting potential benefits of AI and Machine Learning for Deloitte’s diverse government clientele and their potential use cases, contributing to the incremental adoption of AI from clients.• Presented modeling progress for Deloitte partners and leadership stakeholders in a monthly status delivery.• Completed 6 month AI and Machine Learning training through the Deloitte AI Academy.

Gave feedback on econometric analysis in papers, conducted research on energy issues stemming from Russia’s invasion of Ukraine, assisted running economics experiments in ZTree, reformatted papers in LaTeX in preparation for publishing, and learned working knowledge of how to download and use Otree.

Taught 2 Econometrics lab courses during the 2021-2022 school year through the Peer Learning Associate Program, assigned with walking students through homework assignments, assisting students with programming in STATA, and hosting exam review sessions.

Ensured the safety of patrons, trained for rescue, and cleaned pools and facilities.