Summary
Derrick Carr is a Senior Performance Engineer with eight years of experience applying physics-grade data science and machine learning to operational problems in solar energy and astrophysics. He builds production ML models and algorithms that estimate production and revenue loss down to combiner-box level, automates ETLs and webdrivers to flag critical transformer (DGA) events, and scales fleet-wide data pipelines using Python and SQL. With a PhD in Physics from UNC Chapel Hill, he translated techniques from large-scale galaxy surveys—supervised learning, anomaly detection, MCMC, and time-series analysis—into tangible performance improvements for utility-scale solar fleets. Derrick routinely handles datasets of millions of rows and has engineered modular, automated pipelines for multiple data products and observability workflows. He blends scientific rigor with production engineering, often spotting subtle data-quality signals that prevent catastrophic equipment failures before they occur. Based in the Raleigh–Durham area, he thrives on turning complex, noisy data into actionable insights that save energy and revenue.
7 years of coding experience
2 years of employment as a software developer
High School Diploma, High School Diploma at St. Andrew's Episcopal School
Bachelor of Science - BS Astrophysics, Bachelor of Science - BS Astrophysics at Haverford College
Doctor of Philosophy - PhD Physics, Doctor of Philosophy - PhD Physics at The University of North Carolina at Chapel Hill