Finance professional who uses analytics to make decisions, with an M.S. in Finance and a B.S. in Finance and International Trade, now completing an M.S. in Business Analytics (STEM-designated, Data & Methods concentration) at Boston University's Questrom School of Business, expected January 2027. More than eight years in finance roles before the MSBA: structured-finance credit analysis at S&P Global, AML and enhanced due diligence at Scotiabank, and financial planning and business development at Cine Colombia, where I directed exhibition and pricing strategy. My work centers on credit and prepayment risk, forecasting and stress testing, and turning model output into a decision a business can act on.
- M.S. in Business Analytics (STEM-designated), Data & Methods Concentration, Boston University, Questrom School of Business, expected Jan 2027
- M.S. in Finance, Universidad de los Andes, Apr 2021
- B.S. in Finance and International Trade, Universidad del Rosario, Oct 2014
| Repository | What it is | Methods |
|---|---|---|
| mortgage-prepayment-rate-regimes | Individual capstone research paper using Freddie Mac loan-level data to analyze voluntary mortgage prepayment across the 2018 and 2020 origination cohorts. Tests how well predictive models transfer across interest-rate regimes: they keep their ranking power but lose their calibration. | Discrete-time hazard panel, logistic regression, gradient boosting, cross-regime transfer test, calibration analysis |
| loan-default-risk-prediction | Team project predicting loan default risk for approximately 87K LendingClub loans from New England and New York. | Logistic regression, decision tree, random forest, XGBoost, GridSearchCV |
| european-pharmaceutical-rd-analysis | Team project examining the European Medicines Agency’s authorization landscape: how regulatory pathways combine, how they relate to authorization outcomes, and how human and veterinary medicines differ. | Clustering, Apriori association rules, Fisher's Exact Test |
| analytics-massachusetts-loan-risk | Team project integrating LendingClub loan data with U.S. Census (ACS) socioeconomic indicators to explore how borrower characteristics and local economic conditions relate to default risk in Massachusetts. | Data integration, exploratory analysis, choropleth mapping |
- Programming and machine learning: Python (pandas, NumPy, scikit-learn, statsmodels), SQL, R, PySpark; supervised and unsupervised ML, neural networks, experiment design
- Data and BI tools: Power BI, Tableau, Excel (VBA, Power Query), Oracle, Google Cloud / BigQuery, Gurobi optimization
- Finance: financial modeling and forecasting, credit and structured-finance risk, stress testing, AML/EDD, financial reporting