Stress Capital Buffer Modeling Group - Capital Management

Risk Modeling and Analytics - Credit & Obligor Risk Analytics

Data Scientist, Predictive Analytics Team LeadFinance - Capital Planning Led a team of data scientists and analysts (4 headcounts) that oversaw all functions of Predictive Analytics and Cross Assets Aggregation utilizing novel approaches and cutting edge machine learning techniques to enhance capabilities of the Risk-weighted Assets (RWA) forecasting space. Consistently produced high frequency forecasts of RWA under multiple regulatory approaches for both Citigroup and Citibank NA with accuracies under 1% compared to actual RWA balances. Developed and put into production an Early Estimate tool to define, detect and mitigate any unexpected movements from the prior month's RWA balance and variance to other forward looking forecasts. Developed a Balance Sheet Simulation tool that utilizes historical time series data and a sophisticated auto ARIMA modeling approach with outlier detection to provide an alternative forecast with the use of FP&A data inputs. Developed a macroeconomic sensitivity analysis framework for RWA to satisfy CCAR remediation efforts. Produced detailed reports for senior leaders outlining forecasts and projections with daily, weekly and quarterly variance.

Data ScientistFinance - Capital Planning Developed a robust framework to forecast firm-wide RWA and SLE for high frequency outlooks for all products in Citi's balance sheet using cutting edge programming languages and modeling techniques. Developed capabilities to measure performance of balance sheet inputs and outputs to drive efforts around risk and opportunities. Developed new machine learning models to forecast risk weights for individual advanced retail cards portfolios. Managed the group's summer intern, overseeing the development of a new modeling proactice to enhance CCAR forecasting capabilities.

Instructor of Record for Principles of Microeconomics and Macroeconomics. These courses were offered to all students, satisfying general education requirement and as part of the core instruction for the business school. Provided 3 hours of in class instruction each week as well as various opportunities for out of class instruction to enhance students' learning. Class sizes ranged from 32-65 students of all grade levels. Consistently achieved very high teaching evaluations: 4.5-4.8/5.0, with better than average response rates: 75-100%.

Assisted in the course development and instruction of graduate, undergraduate and MBA level courses in the Department of Economics.