About

Nikhil Gupta is a highly accomplished Lead Data Scientist, Solutions Architect, and Machine Learning Engineer with over 13 years of experience in the industry. He has a proven track record of delivering results and is a passionate analytics leader. Nikhil is currently working as a Data Scientist in the Supply Chain domain at o9 solutions, where he is building the next generation Forecasting, Capacity Allocation, and Replenishment Planning engine using machine learning and AI. Prior to this, Nikhil held various leadership roles in the analog modeling organization at Texas Instruments, where he led a cross-functional team of engineers who delivered both offline and online analytical models for the complete Analog portfolio. He also worked as a Machine Learning Engineer, building tools to automate the analog integrated circuit design process using statistical and machine learning techniques. In addition, Nikhil has developed a modified “stacking” algorithm to improve the accuracy of existing ML models and guided interns on the development of circuit similarity (graph) algorithms to enable design clustering to aid with efficient IP reuse and portfolio refresh. He has filed 6 provisional patents during his tenure at Texas Instruments. Nikhil is a Master of Science in Data Science with a Machine Learning Specialization from Southern Methodist University. He also holds a Master of Science in Electrical Engineering from Arizona State University and a Bachelor of Engineering in Electronics and Communication from RV College of Engineering. He completed his distance learning in Statistics from North Carolina State University. Nikhil also has a High School Diploma from St. Xavier's, Kolkata. Nikhil is proficient in various technologies, including Data Science, Research Science, ML, Senior Software Engineering, Software Engineering, and AI. He is an active writer and regularly writes about Data Science and related topics. Nikhil is a PyCaret Core Developer and can be connected through LinkedIn, Twitter, Medium, and GitHub.