Brandon Williams

Lead end-to-end design, implementation, and production deployment of wind and solar power forecasting algorithms using large spatiotemporal data and hybrid ML approaches to improve operational forecasting accuracy.

Owned technical strategy and productionized algorithms for energy price forecasting and storage optimization, delivering scalable ML systems and data pipelines for operational decision-making.

Led development and deployment of advanced ML and statistical techniques on large spatiotemporal datasets to optimize energy forecasting and storage, ensuring research rigor and production reliability.

Developed models for energy price forecasting and storage optimization and collaborated cross-functionally to productionize ML solutions supporting business objectives.

Designed and deployed ML algorithms including marketing spend optimization that improved cost-to-revenue ratio by 57%, and built recommendation and funnel prediction systems.

Built custom data products for clients including anomaly detection, journey optimization, fraud detection, and claims routing, enabling improved operational outcomes.

Built end-to-end ML products to classify and recommend job applicants, improving hiring decisions from R&D through production deployment.

Grade: 3.36

Completed a Master of Science in Mathematics with coursework in scientific computing, linear algebra, and mathematical statistics; thesis developed a parallelized finite-element Stokes solver comparing Julia and Matlab performance.

Grade: 3.67

Activities and societies: Dean's List (2011–2013)

Completed a Bachelor of Arts in Mathematics with a minor in Economic Theory, including coursework in probability, data structures, econometrics, and mathematical modeling; earned Dean's List honors 2011–2013.