Neel Jambhekar
About Neel Jambhekar
Neel Jambhekar is a Machine Learning Engineer with a background in Mathematics and Engineering. He has experience in developing recommendation systems, deploying machine learning models, and working with natural language processing.
Current Role at NanoNets
Neel Jambhekar has been employed as a Machine Learning Engineer at NanoNets since 2021. In this role, he focuses on developing and deploying machine learning models, contributing to the company's efforts in enhancing automation and efficiency in various applications. His work involves utilizing advanced machine learning and deep learning techniques to solve complex problems.
Previous Experience in Data Science
Prior to his current position, Neel Jambhekar worked at Belong.co as a Data Scientist from 2017 to 2020. His responsibilities included developing recommendation and ranking systems, where he applied machine learning techniques. He also served as a Data Scientist II at Ericsson from 2020 to 2021, further expanding his expertise in data-driven solutions.
Educational Background
Neel Jambhekar studied at the Birla Institute of Technology and Science, Pilani, where he earned a Bachelor of Engineering (BE) in Electrical and Electronics Engineering from 2013 to 2018. He continued his education at the same institution, achieving a Master of Science (MSc) in Mathematics during the same period. This strong educational foundation supports his work in machine learning and data science.
Internships and Early Experience
Neel Jambhekar gained early experience as a Summer Trainee at the National Aluminium Company Limited in 2015 for two months. He also completed a research internship at the Centre for Modeling and Simulation at the University of Pune in 2016, where he contributed to various projects. These experiences provided him with practical insights into the application of engineering and data science principles.
Expertise in Machine Learning and NLP
Neel Jambhekar has developed expertise in machine learning, particularly in recommendation and ranking systems. He has experience in deploying machine learning models to production environments and handling time-series data problems. Additionally, he specializes in text and content understanding, utilizing Natural Language Processing (NLP) and Information Retrieval (IR) concepts to address complex data challenges.