Jitendra Shirolkar
About Jitendra Shirolkar
Jitendra Shirolkar is a data scientist with extensive experience in machine learning and engineering. He has worked at various companies, including Solliance, AIS, and Baseline, and has contributed to multiple peer-reviewed publications.
Work at AIS
Jitendra Shirolkar has been employed at Applied Information Sciences (AIS) as a Data Scientist since 2020. His role involves leveraging data science techniques to solve complex problems in the Washington DC-Baltimore Area. During his tenure, he has contributed to various projects that focus on machine learning and data analysis.
Current Position at Solliance
Since 2019, Jitendra Shirolkar has worked as a Data Scientist at Solliance in the Washington D.C. Metro Area. In this position, he applies his expertise in data science to enhance operational efficiency and develop innovative solutions. His work at Solliance has been integral to the company's data-driven initiatives.
Education and Expertise
Jitendra Shirolkar holds a Ph.D. in Mechanical Engineering from Brigham Young University, where he studied from 1990 to 1996. He also earned a B.Tech. in Mechanical Engineering from the Indian Institute of Technology, Bombay, from 1984 to 1988. His educational background provides a strong foundation for his work in data science and machine learning.
Professional Experience at MicroStrategy
Jitendra Shirolkar has held multiple leadership roles at MicroStrategy. He served as Senior Director of Web and Mobile Engineering from 2010 to 2013, followed by a position as Vice President of Software Engineering from 2013 to 2014. He later became Vice President and Group Product Owner from 2014 to 2015. His experience at MicroStrategy involved overseeing product development and engineering teams.
Research Contributions
Jitendra Shirolkar has authored seven peer-reviewed journal publications and two conference papers. His research includes developing an unsupervised, autoencoder-based anomaly detection model for server monitoring and exploring deep neural network architectures for machine learning solutions using time-series vehicle telematics sensor data.