Summary
Isaac Khader is a Lead Data Engineer with a decade of experience translating rigorous physics and electrical engineering training into practical data science and production analytics. He combines deep expertise in signal processing, time-series analysis, and control systems from his NIST research with modern ML and NLP pipelines built in Python and deployed at scale. His background includes low-level embedded implementations (FPGA/DSP) and advanced statistical techniques like Kalman filtering and modified Allan deviations, giving him a rare ability to bridge hardware-near measurement problems and high-level data products. At NYC TLC he progressed from Data Engineer to lead, and earlier roles saw him deliver full-stack data science solutions including web scraping to production-grade NLP models. He holds master’s degrees in Electrical Engineering and Computer Science and several published papers on optical systems, reflecting both scholarly rigor and production impact.
10 years of coding experience
8 years of employment as a software developer
Master's degree, Computer Science, Master's degree, Computer Science at Georgia Institute of Technology
Bachelor's degree, Physics, Bachelor's degree, Physics at Reed College
Master's degree, Electrical and Electronics Engineering, Master's degree, Electrical and Electronics Engineering at University of Colorado Boulder