Dipanjan Sen
About Dipanjan Sen
Dipanjan Sen is the Director of Data Science at Placer.ai, where he has worked since 2020. He has extensive experience in data science, having previously held positions at M Science and Palantir Technologies, and holds advanced degrees in Computational Materials Science and Mechanical Engineering.
Work at Placer.ai
Dipanjan Sen has been serving as the Director of Data Science at Placer.ai since 2020. In this role, he oversees data science initiatives and leads the development of analytical products that leverage geolocation and consumer spending data. His leadership has been instrumental in creating tools that assist clients in the commercial real estate and retail sectors.
Previous Experience at M Science
Prior to his current position, Dipanjan Sen worked at M Science as the Data Science Team Lead from 2018 to 2019. During his tenure in the Greater New York City Area, he contributed to the development of data-driven insights and strategies, enhancing the company's analytical capabilities.
Experience at Palantir Technologies
Dipanjan Sen worked as a Data Scientist at Palantir Technologies from 2011 to 2017. His six-year experience in the Greater New York City Area involved working on complex data analysis projects, contributing to the development of solutions that address various data challenges faced by clients.
Education and Expertise
Dipanjan Sen holds a PhD in Computational Materials Science from the Massachusetts Institute of Technology. He also earned an M.S. in the same field from The Ohio State University. Additionally, he completed a B.Tech. in Mechanical Engineering at the Indian Institute of Technology, Delhi. His educational background provides a strong foundation for his work in data science and analytics.
Achievements in Data Product Development
Under Dipanjan Sen's leadership, a significant data product was developed that provides daily store-level retail sales estimates. This product integrates geolocation and consumer spending data and is utilized by clients in the commercial real estate and retail sectors. It covers sales data for approximately 500,000 stores and 1,000 retailers over a five-year period.