Sumanth Munnangi
About Sumanth Munnangi
Sumanth Munnangi is a Data Scientist currently working at GAIN Credit and a Research Student Analyst at the University of California, Davis. He has extensive experience in data science, having held various roles in companies such as Mu Sigma Inc., PayU, and Team Ojas, and has a strong educational background in business analytics and engineering.
Work at GAIN Credit
Sumanth Munnangi currently serves as a Data Scientist at GAIN Credit, where he has been working since 2022. In this role, he focuses on applying data science techniques to enhance business operations. He developed an anomaly detection tool that significantly reduced the number of anomalies by tenfold, which streamlined the investigation process for data analysts. His contributions have positively impacted various business verticals through integration and automation efforts.
Education and Expertise
Sumanth Munnangi holds a Master of Science in Business Analytics from the University of California, Davis - Graduate School of Management, which he completed in 2023. Prior to this, he earned a Bachelor of Technology in Electronics and Communications Engineering from Vellore Institute of Technology in 2019. His educational background equips him with a strong foundation in data analysis and business intelligence.
Professional Experience
Sumanth has a diverse professional background in data science and analytics. He worked at Mu Sigma Inc. as a Decision Scientist from 2019 to 2021 and later as a Team Lead in the same role for 11 months in 2021. He also held positions as an Operations Manager at Team Ojas in 2016 and as a Sales and Marketing Intern at Focus Academy For Career Enhancement in 2018. Most recently, he worked as a Data Analyst at PayU from 2021 to 2022.
Technical Skills
Sumanth Munnangi has expertise in designing and implementing cloud-based big data solutions on both Azure and AWS platforms. He has developed a Python module for feature engineering, machine learning, and ETL processes tailored for multi-dimensional time series data stored on SQL servers. His technical skills enhance his ability to analyze and interpret complex datasets effectively.