About

Daniel Griffin is a Senior Applied Machine Learning Scientist at Amazon with over 10 years of experience in Data Science. He has worked on over 50 data science projects, including research and real-world AI/ML applications. Daniel is always exploring the latest technical ML research and implementing the latest ML packages. He specializes in Advanced Statistical ML-based methods, Causal Discovery/Representation/Inference, Reinforcement Learning, Deep Generative Models, and advanced Deep Probabilistic Neural Network Methods. At Amazon, Daniel drives innovative AI solutions for lab126, spanning AI product features for Amazon devices, ML analysis around what makes Amazon products good or bad, developing tools for improving the development of Amazon devices, using AI for prescribing decisions and policies, and developing novel and advanced AI methods. He works on both the development and production stages of multiple projects to advance, produce, and deploy predictive and reasoning models. Daniel also works with and presents to the larger Amazon AI community about cutting-edge Machine Learning methods, mathematics, tools, and frameworks. Prior to Amazon, Daniel worked at Cisco as a Staff (Lead) Data Scientist, where he exercised his advanced data science expertise designing, developing, and deploying models and analysis for high impact projects. He also performed technical project management and consulting for data-driven initiatives. Daniel holds a Master's degree in Computer Science (ML/AI) from the University of Wisconsin-Madison and a Bachelor's degree in Computer Engineering from the University of Cincinnati. He has also conducted research on machine learning for irregular time series data sets, with particular validation against electronic health record data. Overall, Daniel is a highly experienced and knowledgeable Data Scientist with expertise in ML, AI, Reinforcement Learning, Docker, and Neural Networks. He is always open to discussing his projects and experience and is passionate about developing new methods for reasoning about data generated over time.