Amit Madhup
About Amit Madhup
Amit Madhup is a Senior Manager in Data Science - Product Analytics at Expedia Group, with extensive experience in analytics and data science across various roles and locations.
Current Role at Expedia Group
Amit Madhup is currently a Senior Manager in Data Science - Product Analytics at Expedia Group. He has been in this role since 2022 and is based in Gurugram, Haryana, India. In this capacity, he manages data science and analytics initiatives that support product development and business intelligence goals.
Previous Experience at Expedia Group
Before being promoted to his current role, Amit Madhup held several positions at Expedia Group. He served as Manager of Data Science - Product Analytics (2021-2022) in Gurugram, Haryana, India, and Manager of Analytics (2020-2021) in the Vancouver, Canada Area. Prior to these roles, he was a Data Scientist III (2019-2020) and a Senior Business Analyst (2017-2019) in the Greater Seattle Area. These roles have seen him work across various locations, enriching his global perspective and expertise.
Early Career Roles
Amit Madhup has a diverse background in analytics and engineering. He worked as an Advanced Analytics Intern at Charles Schwab in Austin, Texas, for 2 months in 2016. Before that, he spent three years at Honeywell Technology Solutions, Inc., in Bengaluru, India, as an Engineer (2012-2015). These roles provided him with foundational skills and experience in data science and analytics.
Educational Background
Amit Madhup holds a Master of Science (M.S.) degree in Business Analytics and Project Management from The University of Connecticut School of Business (2015-2016). He also earned a Bachelor of Engineering (B.E.) in Computer Science from Rajiv Gandhi Prodyogiki Vishwavidyalaya (2007-2011). His educational background has equipped him with both theoretical knowledge and practical skills in business analytics and computer science.
Technical Proficiencies and Skills
Amit Madhup is proficient in a range of analytical tools, including RStudio, Jupyter, and Qubole. He is skilled in statistical techniques such as K-Means Clustering, Text Analytics, and Time Series Forecasting. He is also experienced in using various programming languages and databases, including R, Python, SQL, Hive/Presto, MS SQL Server, Hadoop, AWS S3, and Spark. Additionally, he utilizes advanced packages like scikit-learn, gensim, and nltk for data analysis and predictive modeling.