Rohit Sharma
About Rohit Sharma
Rohit Sharma is a Director at KPMG India with extensive experience in mechanical engineering and analytics. He has held leadership roles in several prominent companies, including Rolls-Royce and GE Energy, and specializes in machine learning applications for predictive analytics.
Work at KPMG India
Rohit Sharma currently holds the position of Director at KPMG India, where he has been employed since 2022. His role involves overseeing various projects and initiatives within the organization, leveraging his extensive experience in engineering and analytics. KPMG India is known for its consulting services, and Sharma's expertise contributes to the firm's capabilities in delivering solutions to clients.
Education and Expertise
Rohit Sharma has a strong educational background in engineering and business analytics. He earned a Bachelor's degree in Mechanical Engineering from Birla Vishwakarma Engineering College, followed by a Master's degree in Mechanical Engineering from the University of Florida. Additionally, he completed an Executive Program in Business Analytics at the Indian Institute of Management, Calcutta. His specialization includes the application of Machine Learning for predictive and prescriptive analytics, enhancing software tool development.
Background
Rohit Sharma's professional journey spans multiple prestigious organizations. He began his career at Alstom Power as a Lead Engineer and later worked at GE Energy as a Global Technical Leader. His tenure at Rolls-Royce included roles as Engineering Manager and Chief/Senior Manager in the partnership with Infosys. Throughout his career, he has gained experience in managing teams across various countries, including India, the US, and Mexico.
Achievements
Rohit Sharma has made significant contributions in his field, particularly in the aerospace sector. He has led the development of aerospace gas turbine components through their entire life cycle, from concept design to service and repair support. His leadership in deploying Statistical and Machine Learning models has resulted in substantial cost savings and productivity improvements, amounting to over $10 million in service/repair cost savings and over $1 million in productivity enhancements.