Scott Tarlow is a data scientist at Plus Power, focusing on forecasting energy prices and designing autonomous energy trading algorithms for grid-scale battery storage systems. His career has spanned the aerospace, defense, and industrial sectors, where he applied machine learning techniques to address key operational challenges, including fault and anomaly detection, demand forecasting, information extraction, and disinformation modeling. Scott’s research interests extend into AutoML, Data-Centric AI, and physics-based machine learning.
Scott’s background includes significant teaching experience. He has taught Data Science and Python courses at General Assembly and has been an instructor for UC Davis and Georgia Tech programs. His passion for education is intertwined with his technical expertise, offering a unique perspective on data science applications across industries.
Scott has worked for The Aerospace Corporation and Hypergiant in his professional journey. He has consulted for clients such as the US Air Force, Georgia Institute of Technology, Salesforce, Chesapeake Energy, Stellantis, and Sumitomo. He is well-versed in deep learning, online learning, computer vision, generative AI (LLMs and neural diffusion), time series forecasting, search and recommender systems, and anomaly detection.
Earlier in his career, Scott was selected as an East Asian Summer Pacific Institute Fellow for the National Science Foundation in Taiwan, where he supported seismic wave event research. This opportunity further deepened his expertise in geophysics and data analysis.
Scott holds an MS in Analytics from the Georgia Institute of Technology, an MS in Geophysics from Southern Illinois University Carbondale, and a Bachelor’s in Physics from Wheaton College, Massachusetts.