Samuel Briones

Samuel Briones is a data scientist with significant experience in data analysis and algorithm development. Currently employed at The Society of St. Vincent de Paul in Arizona since October 2021, Samuel implemented a data consolidation algorithm that reduced manual review by 95% and transitioned automations to Python on Azure Databricks, achieving a 60% decrease in failures and an 80% simplification in resolution processes. Prior to this, Samuel worked as a data scientist at American Express from February 2020 to October 2021, where a Random Forest and XGBoost ensemble model was developed to enhance Change Request Risk Assessment accuracy to 90%, and sentiment analysis reports were automated using Natural Language Processing. Additionally, during a tenure as a data analyst at Brigham Young University - Idaho in late 2019, Samuel created a dashboard for analyzing college drop-out and retention rates and implemented machine learning models to identify key factors affecting student drop-outs. Samuel holds a Bachelor of Applied Science in Data Science from Brigham Young University - Idaho, earned in 2019.