Santiago Olivar Aicinena
About Santiago Olivar Aicinena
Santiago Olivar Aicinena is a Distinguished Data Scientist currently employed at Noodle.ai in Chicago, Illinois. He has a background in analytics and economics, with previous roles at Blue Cross and Blue Shield, TransUnion, Banco de México, and COFEMER.
Work at Noodle.ai
Santiago Olivar Aicinena has served as a Distinguished Data Scientist at Noodle.ai since 2018. His role involves applying advanced data science techniques to solve complex problems and improve operational efficiencies. He has contributed to various projects that leverage machine learning and analytics to drive business insights. His expertise in data-driven decision-making is a key asset to the organization.
Education and Expertise
Santiago Olivar Aicinena holds a Master's degree in Analytics (MSiA) from Northwestern University, which he completed in 2017. He also earned a Bachelor's degree in Economics from Instituto Tecnológico Autónomo de México in 2012. His educational background provides a strong foundation in both analytical and economic principles, enhancing his capabilities as a data scientist.
Background
Santiago Olivar Aicinena has a diverse professional background in data science and analytics. He began his career as a Financial Researcher at Banco de México from 2013 to 2016, followed by a role as Head of Department at the Federal Commission for Regulatory Improvement (COFEMER) from 2011 to 2013. He transitioned to the private sector as an Analytics Consultant at TransUnion from 2016 to 2017, and later took on an Associate Intern position in Data Science at Blue Cross and Blue Shield of Illinois, Montana, New Mexico, Oklahoma & Texas.
Achievements
During his tenure at Noodle.ai, Santiago Olivar Aicinena has developed an internal analytics package utilizing unsupervised learning methodologies to enhance team collaboration and expedite data-driven decision-making. He has also implemented machine learning methodologies to leverage both internal and external data for predicting key performance indicators. Additionally, he created a Spark pipeline that enabled interactive and scalable solutions for machine learning projects.