Scott Sims is a systems engineer and data-savvy AI practitioner with 16 years of experience, currently applying his computational science and aerospace engineering training to build robust simulations and analytics pipelines. He combines hands-on expertise in Python, PyTorch, CUDA, MATLAB/Simulink, and PySpark with a track record of accelerating physics-driven simulations and verifying system redesigns that delivered ~20% cost savings. A former graduate researcher at Georgia Tech, Scott developed a novel deep-learning approach for bubbly fluid flow and brings strong numerical-algorithms and uncertainty-quantification skills to complex engineering problems. His background as an educator and tutor—where he improved student GPAs and taught over 1,000 learners—gives him a distinctive focus on explainability, cognition, and human-centered model communication. At Honeywell and in startup roles he has bridged DevOps, CI-driven SITL simulation, and hardware-in-the-loop testing to produce stakeholder-ready technical reports with extensive visualizations. He is currently growing as an AI engineer and data analyst with interests in production ML platforms like SageMaker and Palantir Foundry.
16 years of coding experience
3 years of employment as a software developer
Master of Science - MS, Aerospace, Aeronautical and Astronautical/Space Engineering, Master of Science - MS, Aerospace, Aeronautical and Astronautical/Space Engineering at Georgia Institute of Technology
Post-Baccalaureate, Mathematics, 4.0, Post-Baccalaureate, Mathematics, 4.0 at University of North Georgia
Contributions:67 commits, 3 pushes, 4 issues in 7 years 9 months
factorypage-objectrubyselenium-webdriverselenium
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.