Shreeniwas Kulkarni
About Shreeniwas Kulkarni
Shreeniwas Kulkarni is a Data Engineer specializing in predictive analysis for renewable energy systems, currently employed at Clearway Energy Group in San Francisco. He has a background in computer engineering and computer science, with experience in various roles including software engineering and business intelligence internships.
Work at Clearway Energy Group
Shreeniwas Kulkarni has been employed at Clearway Energy Group since 2019, serving as a Data Engineer and Analytics and Science professional. His role focuses on utilizing data engineering techniques to support renewable energy initiatives. He is based in San Francisco, California, where he contributes to the company's efforts in optimizing energy generation models.
Education and Expertise
Shreeniwas Kulkarni holds a Bachelor of Engineering in Computer Engineering from Savitribai Phule Pune University, which he completed from 2006 to 2010. He further advanced his education by earning a Master of Science in Computer Science from Arizona State University between 2011 and 2013. His academic background supports his expertise in data engineering and analytics, particularly in the renewable energy sector.
Background in Data Engineering
Prior to his current role, Shreeniwas Kulkarni gained experience as a Data Engineer III at NRG Energy, where he worked for six years starting in 2018. His previous positions include a Software Engineering Intern at PayPal and a Business Intelligence Intern at Leslie's Poolmart, Inc. He has also worked as a Research Aide and Instructional Aide at Arizona State University, contributing to his comprehensive understanding of data analysis.
Technical Skills and Tools
Shreeniwas Kulkarni specializes in predictive analysis for renewable energy systems, focusing on turbines and solar inverters. He employs classification algorithms such as Line Similarity and K-means to identify performance issues in energy models. Additionally, he utilizes Spotfire for data visualization and analysis, enhancing reporting and tracking improvements in energy generation models.