Robert Beatty

Robert Beatty has had a long and varied career in data science. Robert began as a Research Assistant at the Georgia Institute of Technology in 2008, then moved on to become a Research Scientist at Brandeis University in 2009. In 2012, they took on the role of Graduate Teaching Assistant at Kennesaw State University. In 2013, they became a Data Scientist at AT&T, where they performed a topic analysis using LDA on broadband users tech support notes and analyzed historical data using random forests. In 2015, they moved to CNN, where they developed requirements for building out CNN Digital’s big data environment. In 2016, they took on the role of Machine Learning Engineer at Ionic Security, where they defined and implemented conversion of customer audit records of encryption key events generated by Ionic's ABAC policy engine. In 2019, they became a Senior Data Scientist at Equifax, where they productionized graph creation process for use by data scientist and performed anomaly detection of suspicious log events within a customer facing product. Robert currently holds the position of Senior Data Scientist at Momnt, which they began in 2022.

Robert Beatty obtained a Master of Science (M.S.) in Applied Statistics from Kennesaw State University between 2012 and 2014. Robert also obtained a B.S. in Physics from the Georgia Institute of Technology between 2007 and 2009. In addition, Robert Beatty has obtained several certifications from Coursera, including "Convolutional Neural Networks in TensorFlow" (October 2021), "DeepLearning.AI TensorFlow Developer" (October 2021), "Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning" (October 2021), "Natural Language Processing in TensorFlow" (October 2021), "Sequences, Time Series and Prediction" (October 2021), "Deep Learning" (January 2021), "Sequence Models" (January 2021), "Convolutional Neural Networks" (December 2020), "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization" (December 2020), "Neural Networks and Deep Learning" (December 2020), "Structuring Machine Learning Projects" (December 2020), and "Bayesian Methods for Machine Learning (with Honors)" (April 2018).