Andrew Carl is a senior machine learning engineer and data scientist with a rare blend of aerospace mechanical engineering and 38+ years of multidisciplinary systems experience, and eight years focused on applied ML and data science. He builds production-grade, distributed Python systems—most notably the Ray-powered 2StrikeTrader backtester that processed over a billion simulated trades and leveraged 1.4 trillion ticks—and publishes original research in "TransDimensional Machine Learning" using hybrid NEAT reinforcement approaches with UMAP/HDBSCAN embeddings. His career spans aerospace giants and startups, delivering thermal/structural toolchains that convert NASTRAN models into system-level thermal analyses and inventing cost-saving utilities like BDFUTIL/BDFUTIL2 and MODEL BUILDER. He combines hands-on embedded firmware, computer vision and OCR pipelines for massive image datasets with large-scale time-series clustering and custom tooling such as GITHUB2VEC and the GitHub AI "Brain-of-Brains" search utility. Based in Las Vegas, Andrew is comfortable bridging deep physics-based simulation and cutting-edge ML, and often applies evolutionary optimization to control architecture and hyperparameters in both embedded and cloud-distributed environments.
8 years of coding experience
4 years of employment as a software developer
Certificate of Completion, Data Science Immersive, Certificate of Completion, Data Science Immersive at General Assembly
Certificate of Completion, IBM Data Science Professional, Certificate of Completion, IBM Data Science Professional at Coursera
Bachelor's of Science, Natural Resource Management, Minor - Applied Statistics, Bachelor's of Science, Natural Resource Management, Minor - Applied Statistics at State University of New York College of Environmental Sciences and Forestry
Contributions:36 commits, 29 pushes, 1 branch in 3 months
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