CV
Education
- M.S. in Mathematical Modeling, Statistics, and Computation, University of the Basque Country
- M.S. in Applied Mathematics, Santo Domingo Institute of Technology
- B.S. in International Business & Finance, Utah State University
- B.E. in Civil Engineering (first year), Santo Domingo Institute of Technology
Work experience
- 2023-2024: Data Scientist, Promotion Optimization (BEES)
- AB InBev
- Duties included:
- Collaborated with regional LATAM team on a promotion optimization algorithm, improving key metrics like ROI, investment, and coverage across various promotional strategies (combos, stepped, among others).
- This involved modeling demand elasticity with a log-log model (XGBoost), optimizing for a chosen metric with cubic splines, rank suggested order arrangements in combos, among other phases.
- Optimized ROI by 48% in A/B test promotional experiments in the market of Panama, over a period of 8 months.
- Developed an algorithm targeting first-time purchasers, optimizing discount allocation and saving on budget.
- Designed and implemented causal inference models (X-Learner, T-Learner, Synthetic DiD) to assess market innovation impacts (ATT, CATE), enabling trend identification at the blocking factor level.
- Collaborated with regional LATAM team on a promotion optimization algorithm, improving key metrics like ROI, investment, and coverage across various promotional strategies (combos, stepped, among others).
- 2022-2023: Data Scientist, Product Recommendation
- Santa Cruz Bank
- Duties included:
- Optimized the product recommendation (NBA) feature pipeline from having < 20 to > 50 variables, into a collaborative filtering, ranking, and ensemble learning approach.
- Enhanced customer segmentation by incorporating digital behavior clustering and psychographic profiling based on commerce data.
- Automated weekly client account reporting via a data pipeline job, boosting productivity 3x and eliminating manual data wrangling.
- Applied NLP techniques (sentiment analysis, n-grams, entity classification) to extract insights from Salesforce CRM interaction data.
- 2019-2022: Data Scientist, Risk Analytics
- Dominican Tax Administration
- Duties included:
- Collaborated in developing the analytics & machine learning feedback system to label taxpayers as risky or not risky.
- Mapped shareholding structures using graph theory (NetworkX, Bokeh) to identify central entities and uncover hidden transactional relationships, improving the detection and prioritization of tax evasion cases by 2x.
- Built models such as linear regression, decision trees, PCA, and clustering.
- Created the Data Warehouse inventory eCatalog, a Shiny (R) solution mapping all data infrastructure used for risk estimations (metadata, data dictionaries, data lineage). This streamlined the process of identifying data sources by 5x.
Skills
- Git
- Python
- R
- Data Science & AI Techniques: Propensity score matching (PSM), Double machine learning (DML), Doubly robust machine learning (DRML), A/B/n testing, RCTs, synthetic controls, among others.
- Spark
- MLflow
- Azure Cloud: Azure Databricks, DL/Hive, Azure Machine Learning Studio, Azure DevOps, Unity Catalog, Delta Live Tables (DLT), and Azure Synapse Analytics. AWS: Amazon SageMaker, Amazon Redshift, Athena, AWS Glue.
- Additional Tools: SQL, MATLAB, Java, CPLEX, Gurobi, PuLP.
Leadership
- AB InBev Analytics Workshop Professor (2024)
- AB InBev Global Hackathon (2023)
- R Programming Mentor (2022-2023)
Last updated: 2025-08-22