Rupali Gharat
About Rupali Gharat
Rupali Gharat is an Associate Data Scientist currently working at Innoplexus in Pune, India. She has experience in data analysis and machine learning, particularly in the solar energy sector, and holds a Bachelor of Technology in Ceramic Sciences and Engineering from IIT (BHU) Varanasi.
Work at Innoplexus
Rupali Gharat has been employed at Innoplexus as an Associate Data Scientist since 2021. In this role, she focuses on data analysis and machine learning applications. Her work includes developing models that enhance business insights and decision-making processes. Rupali's contributions are particularly significant in the context of data-driven strategies within the organization.
Previous Experience at Technex, IIT (BHU) Varanasi
Prior to her current role, Rupali Gharat worked as an Event Coordinator at Technex, IIT (BHU) Varanasi from 2019 to 2020. During her 11-month tenure, she was responsible for organizing events and managing logistics, contributing to the overall success of various initiatives at the institute.
Data Analysis Role at Peacock Solar
In 2020, Rupali Gharat served as a Data Analyst at Peacock Solar for a duration of 2 months. In this position, she focused on analyzing data relevant to the solar energy sector, which provided her with practical experience in data handling and interpretation.
Education and Expertise
Rupali Gharat completed her Bachelor of Technology (BTech) in Ceramic Sciences and Engineering at the Indian Institute of Technology (Banaras Hindu University), Varanasi, from 2017 to 2021. Additionally, she engaged in JEE Coaching at Dakshana Foundation from 2015 to 2017. Her educational background has equipped her with a strong foundation in analytical skills and machine learning concepts.
Machine Learning Model Development
Rupali Gharat developed a machine learning model aimed at identifying customer segments within the solar energy market. This project highlights her ability to apply theoretical knowledge to practical scenarios, showcasing her skills in analytics and model building.