Summary
Felipe González is a Data Scientist with a Ph.D. in Physics and 11 years of professional experience turning massive, messy datasets into actionable insights using Python and C++. He has applied Bayesian statistics and ETL pipelines to analyze multi-terabyte experimental data at CERN and now builds production-ready ML models and data infrastructure for enterprise clients in Montreal. His background in high-energy physics gives him deep expertise in rigorous statistical modeling, distributed computing, and real-time data calibration—skills he’s translated into forecasting, streaming analytics, and BI solutions. At Pharmascience and Gildan he led model deployment, data engineering, and the establishment of data science best practices within cross-functional teams. A proactive communicator and educator, he repeatedly bridges research-grade methods and business needs, quickly adopting new tools and fostering Agile workflows. He enjoys uncovering non-obvious patterns in data, such as weather-driven demand signals and subtle detector-calibration effects, to drive measurable impact.
11 years of coding experience
5 years of employment as a software developer
Spécialiste en mégadonnées et intelligence d'affaires, Data Science, Big Data, BI, Spécialiste en mégadonnées et intelligence d'affaires, Data Science, Big Data, BI at Collège de Bois-de-Boulogne
Bachelor's degree, PHYSICAL SCIENCES, Bachelor's degree, PHYSICAL SCIENCES at Universidad Pedagógica y Tecnológica de Colombia
Universidad de los Andes
Spanish, English, French