Harsh Shrivastav
About Harsh Shrivastav
Harsh Shrivastav is a Data Specialist at Bizongo, where he has worked since 2020. He holds a Post Graduate Diploma in Packaging Science and a Post Graduate Program in Data Science and Business Analytics, and has experience in quality assurance and data analysis.
Work at Bizongo
Harsh Shrivastav has been employed at Bizongo as a Data Specialist since 2020. In this role, he focuses on handling high dimensionality data challenges and addressing issues related to the unavailability of structured data in quality assurance projects. He previously worked as a Packaging Executive - NPD at Bizongo from 2018 to 2020. His experience at Bizongo spans over four years, contributing to the company's data-driven decision-making processes in the Mumbai Area, India.
Education and Expertise
Harsh Shrivastav holds a Post Graduate Diploma in Packaging Science from the Indian Institute of Packaging, where he studied from 2016 to 2018. He also completed a Post Graduate Program in Data Science and Business Analytics at The University of Texas at Austin from 2020 to 2021. Additionally, he earned a Bachelor of Engineering (B.E.) in Mechanical Engineering from ITM Group of Institutions, studying from 2010 to 2014. His educational background equips him with expertise in data analysis, quality assurance, and packaging.
Background
Before joining Bizongo, Harsh Shrivastav gained experience in various roles. He worked as a Junior Project/Site Engineer at Whirlwind Corporation from 2014 to 2015 and as a Technical Sales Engineer at Petromar Engineered Solutions Pvt. Ltd. for six months in 2016. He also completed a six-month internship as a Packaging Intern at Unilever in 2018. This diverse background in engineering and technical sales has contributed to his skill set in data analysis and quality improvement.
Achievements in Data Analysis
In his current role, Harsh specializes in performing univariate and bivariate analysis to interpret data and derive actionable insights. He implements the DMAIC methodology and the Pareto principle to identify root causes and take corrective actions in quality improvement projects. He utilizes tools such as Excel and Metabase for data collection and analysis, aiming to achieve zero defect aspiration goals in his projects.