Jose A. Garcia Ventura Senior ML Engineer

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Senior AI Engineer

Jan 2026 - Current (6 months)

Senior ML Engineer

HP

May 2025 - Nov 2025 (7 months)

LLM Engineer

Invisible Technologies

May 2024 - May 2025 (1 year 1 month)

Data Scientist

AB InBev

Jun 2023 - Sep 2024 (1 year 4 months)

- AB InBev is the largest beer brewer in the world. As a Data Scientist in the Middle Americas Zone (MAZ) Team, I work on the integration of a machine learning model for promotion optimization tailored to LATAM countries. - Utilize forecasting techniques (XGBoost) to determine the demand of beer and NAB's products, to then optimize for optimal personalized discounts, in a way that minimizes investment. That is, search for optimal product discounts from convex demand curve splines aimed at maximizing net revenue, considering pricing strategy constraints. - Work alongside machine learning engineers and business specialists to ensure that promotion optimization pipeline and strategy is aligned with portfolio optimization audiences. - Work o

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Data Scientist

Santa Cruz Bank

Aug 2022 - Jun 2023 (11 months)

• Boosted sales by 10% via a product recommendation production model using collaborative filtering, ranking, ensemble learning, and continuous monitoring. Automated commercial policies by customer profile for optimal market exposure. • Developed classification models for 8-level bank-wide customer segmentation, leveraging multiple data sources to optimize marketing efforts. • Automated weekly report distribution to various teams by building data pipelines in Python, boosting productivity by 30%. • Participated in the data collection part during the implementation of a new CRM (Salesforce) in the bank's core processes, optimizing the use of customer interaction data.

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Data Scientist

Dominican Tax Administration

Jan 2019 - Jul 2022 (3 years 7 months)

Risk Management Optimization Project • Led a team of 4 people specialized in different economic segments to create risk classification levels for taxpayers with the help of advanced analytics. • Increased by 60% the efficiency of selection of taxpayer audits using predictive modelling techniques on tax evasive behaviors in different economic sectors. Created models such as linear regression, decision trees, STAN probabilistic models (MCMC), and clustering. • Diversified the mitigation treatments to be applied by 65%, automating data pipelines matching risk estimations to potential preventive and corrective actions. Data Governance Project • Collected metadata from the data warehouse, data dictionaries, as well as data lineage information,

Jose A. Garcia Ventura

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