Justin Clark is a founder and Chief Data Scientist with nine years of experience applying applied machine learning, Bayesian methods, and time series forecasting to energy and low-carbon challenges. He partners with Fortune 100 clients to translate cutting-edge analytics into measurable business outcomes while mentoring data science teams to operate in fast-evolving AI environments. His prior work built SCADA AI tools and Bayesian hierarchical models that identified events responsible for over 80% of methane emissions, demonstrating an unusual focus on high-impact environmental detection using existing sensor networks. With a background as a petroleum engineer and an M.S. from USC, he blends domain expertise in oil & gas with rigorous data science practice. Based in Reno, he has moved from hands-on data engineering and reservoir analytics to leading commercial forecasting and enterprise AI strategy. Colleagues value his ability to turn complex probabilistic models into actionable decision tools that drive both sustainability and commercial performance.
9 years of coding experience
11 years of employment as a software developer
Master of Science (M.Sc.) Petroleum Engineering, Master of Science (M.Sc.) Petroleum Engineering at University of Southern California
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.