Career

Experience

From Systems Engineering at Universidad del Norte to Data Science at AB InBev — focused on enterprise ML systems that support strategic decisions.

Sep 2025 — Present

Bogotá, Colombia · On-site

AB InBev

·Full-time

Led the continuous improvement and adoption of the investment optimization model across markets, incorporating new data sources, refining predictive logic, and enhancing model interpretability for business stakeholders.
Developed new features and data pipelines to support model scalability and regional rollout, improving accuracy, reducing latency, and enabling deployment in additional LATAM markets.

Feb 2025 — Sep 2025

Bogotá, Colombia · Hybrid

AB InBev

·Contract

Localized and deployed a U.S.-developed investment optimization model for the Colombian market, adapting data sources, business rules, and predictive features to regional dynamics — improving budget allocation efficiency and marketing ROI across key commercial channels.
Fine-tuned and productionized BERT-based NLP models for automated sales order classification, increasing accuracy, reducing manual processing time, and enabling categorization of over 200,000 historical purchase orders.
Partnered with cross-functional and global teams (Colombia, U.S., India) to validate results and align model outputs with business objectives.

Aug 2024 — Jan 2025

Bogotá, Colombia · On-site

AB InBev

·Internship

Supported the Mexico operation of Smart Discounts, a promotion optimization framework, as part of a regional rollout across multiple LATAM markets (Colombia, Mexico, Ecuador). Contributed to data integration, feature validation, and local adaptation.
Designed and implemented an AI-driven ranking algorithm to prioritize promotional offers by customer purchase propensity, enhancing targeting accuracy and campaign effectiveness in the Mexican market.
Developed and maintained data pipelines in Python and SQL, ensuring reliable data flow, scalability, and performance across the modeling workflow.

Jan 2024 — Aug 2024

Colombia · Remote

Outlier

·Part-time

Evaluated and refined AI-generated code, ensuring consistency, functionality, and compliance with best practices — contributing to a 30% reduction in critical errors.
Assessed advanced model capabilities including reasoning, RAG workflows, code generation behaviors, and compliance with quality and security standards.
Identified behavioral issues and improvement opportunities to increase overall model reliability and output quality.

2020 — 2024

Barranquilla, Colombia

Universidad del Norte

·Education

Graduated with a 4.6/5.0 GPA in Systems and Computing Engineering, with focus areas in machine learning, statistics, and data science.
Completed coursework spanning algorithms, distributed systems, databases, and applied mathematics — building the engineering foundation for production ML systems.