About

Freddy Boulton is a Senior Software Engineer at Alteryx with over 6 years of relevant experience in the field of software engineering, data science, and deep learning. He is a skilled professional with expertise in building tools for training deep learning models of driver behavior for autonomous vehicles. Freddy is passionate about reproducible data pipelines and extensible software that can make breakthroughs in data science. Before joining Alteryx, Freddy worked as a Software Engineer at Aptiv, where he developed a software library for efficiently training deep learning models on 60 hours of log data collected from autonomous vehicles. He also researched convolutional neural network architectures for predicting future trajectories of vehicles from log data collected from their fleet of autonomous vehicles. Freddy developed a python package for manipulating map data without consuming all the available RAM and developed a Random Forest model for predicting the lane a vehicle will take in an intersection. He showed an 11% improvement in performance over the existing method. As a Data Scientist at Nielsen, Freddy used t-SNE and K-Means algorithms to create television station clusters for use in Nielsen ratings calculations. He also helped develop a Recurrent Neural Network to predict household demographics based on cable set-top box data. Freddy analyzed DVD sales data and developed a random forest model to predict future sales within 15% relative error. He designed an interface with R Shiny to allow stakeholders to make predictions. Freddy holds a Master’s degree in Statistics and a Bachelor’s degree in Statistics from the University of Chicago. He is proficient in programming languages such as Python, Pytorch, Keras, Tensorflow, and has experience in using tools such as MongoDB, Scikit, Pandas, and cvxpy. Overall, Freddy Boulton is a highly skilled Senior Software Engineer with a strong background in data science and deep learning. He is passionate about developing innovative solutions that can make a significant impact in the field of data science.