• Owned analysis and story development for high-priority requests from CCB Executive Leadership on Chase's comparative performance in the Affluent and High Net Worth customer segments• Redesigned model which evaluates 400K loans annually to ensure fairness across customers – ultimately saving ~$10MM in impact per year• Led redevelopment of tool that automates the end-to-end process of providing causal insights on business practices across lines of business• Managed analysis determining value of customer enrollment in different alerts and development of High Value Alert OKR for Digital Communications• Served as Client Lead on Force For Good project in which we consulted the Delaware Art Museum on optimal CRM platform and charted the migration plan

Exploratory and Predictive Analysis for Leading Electronics Retailer | Python• Planned and conducted weekly meetings to update client on progress and formulate future plans for team of five data science consultants• Merged daily promotions data with weekly inventory data to provide more comprehensive dataset for which forecasting could be conducted• Engineered features from merged dataset to better capture the relationship of used item sales – improving forecasting performance by at least 5% for all six product classes analyzedExploratory Analysis for Be The Match | Python• Transformed patient and donor datasets and trained predictive models to find most important factors to successful transplants in order to drive recommendations to narrow race gap in transplant rate• Recognized as one of top-8 teams and chosen to unveil analytic findings to client’s executive teamPrediction Analysis for Leading Customer Service Company | Python, R• Leveraged text and audio data to train predictive models which classify voice recordings based on sentiment for leading customer service company to better analyze call centers’ performances• Built slide deck to visualize modeling process and presented results of predictive models via F-score• Recognized as one of top-8 teams and chosen to unveil analytic findings to client

• Established data pipeline by utilizing APIs in R for Dr. Ravi Bapna and Dr. Gordon Burtch to conduct causal analysis concerning NCAA’s recent Name-Image-Likeness legislation• Augmented player data at game-level to panel data to allow for difference-in-difference regression analyses

• Served as a fiduciary for state and local governments issuing debt to finance capital projects• Evaluated competitive bids and verified compliance with all local, state, and federal regulations• Analyzed audited financials and pro formas of clients to determine debt ratios and credit quality using MS Excel in order to maximize upcoming credit ratings• Created debt financing models in MS Excel and DBC to minimize clients’ total interest cost for new and refunded issues• Mentored 2 junior analysts across multiple projects to implement knowledge management process including onboarding, financial analysis, and software training

• Classified past grants and loan attributes into specific categories to discover pricing trends • Forecasted approximate rates for future grants using historical pricing and categorical constraints• Synthesized forecasts into commercially viable algorithms available to customers; saving the department an estimated $50K+ annually