CV

Education

• M.S. in Mathematical Modeling, Research, 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

• 2025-2025: Senior Software Engineer, Pricing Analytics
  • HP Inc (via Mphasis)
  • Duties included:
    • Collaborated within the Pricing Data Science team of HP Inc (Sant Cugat), with a data science software refactoring project for various markets, aiming at developing a common architecture for feature engineering, modeling, and deployment of price elasticity and causal inference models.
    • Architected 3 modular Delta Live Tables pipelines in Python to support retail and commercial demand models across North America and EMEA, reducing code duplication by ~11%, enabling region-specific customizations.
    • Built counterfactual price model (LightGBM) to simulate competitor brand prices as if they were an HP brand, to use as confounders in a demand elasticity model.
• 2024-2025: LLM Engineer
  • Invisible Technologies
  • Duties included:
    • Engineer and curate high-quality datasets to fine-tune large language models (LLMs) for expert-level performance in specialized domains, with emphasis on mathematics, multi-step logical reasoning, and research-intensive tasks.
    • Developed Chain-of-Thought (CoT) mathematical reasoning prompts to strengthen model abilities in stepwise problem-solving, multi-hop inference, and inductive logic.
    • Designed adversarial prompts to surface model failure modes in complex tasks, collected source-verified corrections, and built supervised fine-tuning datasets to reduce hallucination rates.
• 2023-2024: Data Scientist, Promotion Optimization (BEES)
  • AB InBev
  • Duties included:
    • Collaborated with regional LATAM team on a promotion (pricing) 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 dynamic 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.
    • Created an RCT module that automated user allocation into control and treatment groups and logged experiment metadata (promotion ID, allocation, blocking factors, timestamps) to a historical registry, reducing experiment design time by ~80%.
    • Designed and implemented causal inference models (X-Learner, T-Learner, Synthetic DiD, DRLearner) to assess market innovation impacts (ATT, CATE), enabling trend identification at the blocking factor level.
• 2022-2023: Data Scientist, Product Recommendation
  • Santa Cruz Bank
  • Duties included:
    • Optimized the product recommendation (NBA) feature pipeline from having less than 20 to over 50 variables, into a two-tower modeling (item-user embeddings) 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
  • DGII
  • Duties included:
    • Collaborated in developing the analytics & machine learning feedback system to label taxpayers as risky or not risky.
    • Built models such as linear regression, decision trees, PCA, and clustering.
    • Helped define sector-specific, multidimensional risk metrics spanning income underreporting, cost inflation, shareholder benefit abuse, and transfer pricing anomalies.
    • Mapped shareholding structures with graph theory (NetworkX, Bokeh), leveraging centrality insights to reveal influential entities and hidden transactional links, improving tax evasion case prioritization by 2x.
    • 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 2x.

Skills

• Python
• R
• SQL
• Git
• Spark
• Data Science & AI Techniques: Propensity score matching (PSM), Double machine learning (DML), Doubly robust machine learning (DRML), A/B/n testing, RCTs, CATE calibration methods, among others.
• Azure Cloud: Azure Databricks, DL/Hive, Azure Machine Learning Studio, Azure DevOps, Unity Catalog, Lakeflow (data orchestration), Delta Live Tables (DLT), and Azure Synapse Analytics. AWS: Amazon SageMaker, Amazon Redshift, Athena, AWS Glue.
• Additional Tools: MATLAB, Java, Gurobi, PuLP, Docker, MLflow, Airflow, Pytest, CI/CD.

Leadership

• AB InBev Analytics Workshop Professor (2024)
• AB InBev Global Hackathon (2023)
• R Programming Mentor (2022-2023)

Last updated: 2025-12-09