About

Hillary Sanders is a highly skilled machine learning researcher and engineer with over nine years of industry experience. She specializes in machine learning, deep learning, neural networks, Bayesian statistics, probability theory, data visualization, ML experimentation, and training pipelines. Hillary is proficient in Python (PyTorch, Keras, TensorFlow, MLflow, Pandas, Numpy, Re), R, Docker, PostgreSQL, AWS services (EC2 instances, Redshift, Elasticsearch, S3, Elastic Beanstalk), Bash, and has a working knowledge of HTML, CSS, and JavaScript (specifically React and Material-UI). Hillary has a talent for breaking down complex ideas into easy-to-understand messages and pushing systems to optimize true goals instead of indirect ones. She is the co-author of the book "Malware Data Science" (No Starch Press), where she wrote the chapters on neural networks. She has also worked as a senior machine learning researcher at Sophos, where she developed and researched deep learning models for security applications. Currently, Hillary works as a senior ML research scientist at Opendoor, where she is responsible for predicting house values with deep neural networks. Hillary has designed and implemented experiments and improvements that have led to a total estimated $48 million in increased profits. She is also the research lead on a team to improve and redesign deep learning training and experimentation pipeline. Hillary holds a double bachelor's degree in Statistics and Environmental Economics & Policy from the University of California, Berkeley. She is highly proficient in research, machine learning, deep learning, data science, Keras, neural networks, TensorFlow, AWS, PyTorch, Python, Docker, JavaScript, Pandas, Elastic Search, UI, and software engineering. With over ten years of relevant experience, Hillary Sanders is a highly skilled and sought-after machine learning researcher and engineer.