Nathan Rooy is a data scientist in New York with nine years of experience applying machine learning to real-world problems across media, urban analytics, and aerospace. He moved from CFD-driven aerodynamic shape optimization and race car design into building end-to-end ML pipelines that mine social signals for site selection and smart-city applications. At Spatial.ai he was an early technical hire who combined NLP, deep learning, spatial-temporal modeling, and custom AWS scraping to deliver production systems; he now builds data-driven features at Shutterstock. Comfortable with both research-grade tools (OpenFOAM, adjoint optimization) and production stacks (TensorFlow, scikit-learn, SQL, Elasticsearch), he brings a pragmatically scientific mindset to messy, spatially dependent data. Unusually, his background blends wind-tunnel validated engineering and publication-grade materials research with hands-on ML deployment experience.
10 years of coding experience
6 years of employment as a software developer
Bachelor of Science (B.S.), Aerospace Engineering, Bachelor of Science (B.S.), Aerospace Engineering at University of Cincinnati
Master of Science (M.S.), Motorsport Engineering, Master of Science (M.S.), Motorsport Engineering at Cranfield University
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