Vipin Dwivedi

Vipin Dwivedi

Senior Data Scientist @ Medallia

About Vipin Dwivedi

Vipin Dwivedi is a Senior Data Scientist at Medallia in India, specializing in data regression and classification models. He has a background in data analysis from various companies and holds a Bachelor of Engineering in Mechanical Engineering from the University of Pune.

Work at Medallia

Vipin Dwivedi has been employed at Medallia as a Senior Data Scientist since 2020. In this role, he specializes in creating data regression and classification models utilizing predictive and inferential data modeling techniques. His work involves leveraging advanced analytics to derive insights from customer data, contributing to Medallia's mission of improving customer experience through data-driven decision-making.

Previous Experience in Data Analysis

Prior to his tenure at Medallia, Vipin Dwivedi held several positions in data analysis. He worked at Whirlpool Corporation as a Data Analyst from 2014 to 2015 in the Pune Area, India. He then transitioned to Eaton - Hydraulics Business, where he served as a Product Data Analyst from 2015 to 2017. Following this, he worked at Oceaneering as a Data Analyst from 2018 to 2019 in Chandigarh Area, India, and at Jumio Corporation as an Assistant Manager from 2019 to 2020 in Jaipur, Rajasthan, India.

Education and Expertise

Vipin Dwivedi earned his Bachelor of Engineering (B.E.) in Mechanical Engineering from the University of Pune, where he studied from 2009 to 2013. He also completed his 10+2 education at St. Mary's University in 2008-2009. His expertise includes time series analysis, spatial analysis, and the development of neural network and classification tree algorithms for customer classification tasks.

Technical Skills and Tools

Vipin Dwivedi has developed proficiency in various technical skills relevant to data science. He has experience using Google Cloud Platform's BigQuery to fetch data into models. Additionally, he utilizes web scraping techniques to extract and organize customer data, enhancing the quality and accessibility of data for analysis.

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