Mickey Warner

Mickey Warner has held a variety of roles in the data science and operations fields. In 2020, they became the Head of Revenue Operations at SalesRabbit, Inc., where they led the general data effort and designed and built data pipelines, processes, automation, and integrations. In 2019, they were an Operations Manager at the same company, where they organized, created, and maintained processes and systems which manage financial data. Mickey also worked as an independent contractor for SalesRabbit, Inc. that year, building a system to automatically bill customers. In 2018, they were a Data Analyst at Boostability, where they built, deployed, and maintained machine learning models on AWS. In 2016, they were a Data Science Intern at Lawrence Livermore National Laboratory, where they collaborated with climate scientists and statisticians to analyze decadal, historical, and pre-industrial control simulations from climate models. In 2015, they were a Data Science Intern at the same company, working with material scientists to fit Bayesian hierarchical models to stress-strain curves. In 2013, they were a Graduate Teaching Assistant and Research Assistant at Brigham Young University. In 2019, they were also a Secondary Math Teacher at American Preparatory Academy.

Mickey Warner obtained a Master's Degree in Statistics from Brigham Young University between 2009 and 2015. Mickey then went on to obtain a Master's Degree in Statistics and Applied Mathematics from the University of California, Santa Cruz between 2015 and 2018. In addition, they have obtained certifications from Coursera in Programming Foundations with JavaScript, HTML and CSS (with Honors), Java Programming: Solving Problems with Software, Programming for Everybody (Getting Started with Python), Python Data Structures, Using Python to Access Web Data, Customer Analytics, Managing Big Data with MySQL, Operations Analytics, People Analytics, Convolutional Neural Networks, Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization, Neural Networks and Deep Learning, and Structuring Machine Learning Projects.