Mohamed Habib is a Data Scientist with 8 years of experience building end-to-end AI systems that span embedded hardware to cloud-hosted applications. He designs and ships production ML solutions—computer vision for fare-evasion detection, LLM integrations on Google Cloud, and optimization algorithms that cut routing compute from hours to minutes. His background blends hands-on embedded work (Raspberry Pi, BeagleBone, custom PCBs) with full-stack software and ETL automation, enabling real-time IoT data pipelines and visualization tools. A 4.0 MS graduate in Electrical and Computer Engineering, he has applied ML to transportation and weather problems with high-impact results (90%+ accuracy, 0.9+ F1) and has experience leading agile teams and CI/CD-driven projects. Notably, he has delivered fielded systems that couple sensor-level engineering with large-model capabilities, bridging the gap between edge data collection and cloud-scale inference.
8 years of coding experience
3 years of employment as a software developer
Bachelor's degree, Computer Science, Bachelor's degree, Computer Science at University Of Aleppo
Master of Science - MS, Electrical and Computer Engineering, 4.0, Master of Science - MS, Electrical and Computer Engineering, 4.0 at University of Oklahoma
Building an LSTM Recurrent Neural Network for Predicting Stock Market Prices.
Contributions:11 commits, 1 PR, 7 pushes in 23 days
stock-marketpythonmarket-pricesdeep-learninglstm
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