Keyur Faldu is a founder and AI leader with 12 years of experience building high-impact machine learning systems across tech and enterprise, currently scaling HyperNorm AI to improve impact causality in finance. He previously led post-click full-funnel optimization at Meta, driving cross-team deep learning and RL solutions that contributed to Shops surpassing $1B in revenue and delivered double-digit ROAS improvements for advertisers. Earlier, as Chief Data Scientist at Embibe he built a 50+ data science organization and an in-house research lab that produced 15+ publications and patents and supported a $180M acquisition. His consultancy and delivery background at McKinsey and prior engineering roles show a rare blend of operational optimization (including a $400M railroad impact) and product-focused ML engineering. Comfortable moving between research, IP strategy, and production at scale, he combines rigorous academic roots from IISc with hands-on experience shipping systems that influence revenue and policy. Notably, he now applies that mix to make financial asset decisions faster and more reliable while advocating elevated AI norms.
12 years of coding experience
17 years of employment as a software developer
ME, Computer Science, ME, Computer Science at Indian Institute of Science
B.E., Information Technology, B.E., Information Technology at Hemchandracharya North Gujarat University
This is a niche collection of research papers which are proven to be gradients pushing the field of Natural Language Processing, Deep Learning and Artificial Intelligence
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