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Esteban Urdiales has a diverse work experience in various roles related to data science and education. Esteban started their career as an Adjunct Professor at Lake Forest College in 2011. Esteban then worked as an Assistant Coordinator to the Summer Research Opportunity Program at Northwestern University, where they mentored students and organized research opportunities. After that, they took on a Post-Doctoral Fellowship in Mathematics at Lake Forest College.

In 2013, Esteban became an Assistant Professor at Marian University Indianapolis, where they taught various subjects and supported the Mathematics Hiring Committee. Esteban also played a role in promoting the department and coordinating accreditation procedures.

In 2017, Esteban worked as a Data Analyst/Scientist for a logistics consulting role.

In 2018, they joined IvySys Technologies, LLC as a Data Scientist, where they implemented neural network models, developed analytics visualization applications, and worked on text data processing.

Esteban then joined Insight Data Science as a Fellow in 2018. Currently, Esteban is working as a Data Scientist at CALIBRE Systems, Inc. starting in November 2020.

Esteban Urdiales completed their education as follows:

Esteban obtained a Doctor of Philosophy (Ph.D.) degree in Applied Mathematics from Northwestern University, where they studied from 2006 to 2010. Prior to that, they earned a Master's Degree in Applied Mathematics from the same institution, studying there from 2005 to 2006.

Before attending Northwestern University, Esteban completed their Bachelor's Degree in Mathematics and Computer Science from the University of Illinois Chicago. Esteban attended this university from 2001 to 2005.

In addition to their formal education, Esteban has also earned several certifications. Esteban obtained the Certified Defense Financial Manager certification from the American Society of Military Comptrollers in February 2023. Esteban also completed various online courses offered by Coursera, including Developing Data Products, Practical Machine Learning, Regression Models, Statistical Inference, Reproducible Research, Exploratory Data Analysis, Getting and Cleaning Data, R Programming, and The Data Scientist's Toolbox. These courses were completed between 2016 and 2017, with the specific completion months varying for each course.