Data Scientist and Economist

Developed and deployed a state-space Kalman filter model for aggregate-market nowcasting and forecasting. Reduced back-tested error by more than 20% and supported executive reporting, monitoring, and business analytics decisions.

Quantified forecast uncertainty through mathematical and statistical methods and Monte Carlo simulation, replacing conservative worst-case confidence bands with calibrated estimates for risk management. Built end-to-end random forest, XGBoost, and LightGBM classification pipelines for affordable-housing loans using Python, SQL, and millions of Snowflake application records.

Addressed internal model-risk audit and federal-regulator inquiries by delivering challenger models and sensitivity analyses across production deployments. Partnered with underwriting and other cross-functional teams to improve processes and model governance.

Owned and delivered a Double Machine Learning framework to evaluate an inventory-management policy. Introduced and adapted the methodology to support business strategy decisions.

Led the team's first heterogeneous treatment-effect analysis, measuring campaign performance variation across seller segments using high-dimensional characteristics. Validated results with Difference-in-Differences modeling, inverse propensity-score weighting, event-study diagnostics, and robustness checks.

Partnered with product managers to translate causal-analysis findings into product design recommendations for differentiated seller targeting.

Applied econometrics and statistics to examine five decades of global cross-border trade agreements. Modeled the Spaghetti Bowl phenomenon using PPML regression and multi-way fixed effects.

Contributed to research later published as Cherif, Hasanov, and Wang (2026), “Sharp instrument: A stab at identifying the causes of economic growth,” in Structural Change and Economic Dynamics.

Developed econometric coding solutions for the IMF MOOC Macroeconomic Forecasting, supporting more than 1,800 participants across 167 countries.

Automated data-processing and advanced analytics workflows in R to improve research efficiency. Supported modeling, data visualization, and reporting activities.

Ph.D. in Economics. Received the Bankard Fund Fellowship Award (2022–2023) and the Snavely Prize for Outstanding Summer Paper Award (2019).

Master of Arts in Economics.

Double major with honors.