Ronald Humberto Castillo Capino

Developed Python applications and PowerBI dashboards integrating SAP PM data, reducing process time by 20% and increasing operational efficiency. Implemented integrations between GitHub, SQL, and Jenkins, cutting manual tasks by 40% and improving data traceability by 50%.

Engineered ETL pipelines and predictive models with PySpark and SQL, increasing vehicle price prediction accuracy by 75%. Led a migration from GCP to AWS and developed an NLP model that reduced response time by 50% and increased conversions by 15%.

Spearheaded machine learning models (TensorFlow, Scikit-Learn) for agro-climatic analysis, improving long-range prediction accuracy by 60%. Coordinated geospatial analysis and created interactive Plotly dashboards, reducing strategic decision-making time by 20%.

Designed and implemented MILP optimization models with CPLEX-IBM, reducing operational costs by 30%. Automated order management using Python and DB2-SQL, cutting cycle time by 35% and increasing order throughput by 25%.

Developed segmentation models (SVM, K-means) and behavioral analyses using Python, PostgreSQL, and MongoDB, increasing targeted marketing efficiency by 15%. Built dashboards with Plotly and QlikView that raised conversion rates by 20% and improved retention by 10%.

Created quantitative models and automated reporting with MATLAB and VBA, reducing report generation time by 50% and increasing operational efficiency by 35%.

Completed a Master's in Artificial Intelligence at Pontificia Universidad Católica de Chile from 2022 to 2023.

Completed a Diploma in Big Data & Data Science at Pontificia Universidad Católica de Valparaíso in 2016.

Earned a Mathematical Engineer degree from Universidad de Santiago de Chile between 2002 and 2011.

Received a Bachelor of Mathematics from Universidad de Santiago de Chile from 2002 to 2007.