Paavni Rattan

Paavni is a Principal Scientist at Keystone, where she leverages her expertise in machine learning, causal inference, and experimental design to drive innovative solutions. Prior to joining Keystone, Paavni built machine learning models for Azure and consumer analytics at Microsoft. In 2017, she joined Amazon's Supply Chain Experimentation team, where she applied causal inference methods to generate valuable insights from experimental and observational data. Her work at Amazon played a pivotal role in informing strategic decision-making and optimizing supply chain operations.

Paavni has also led science teams at Glossier and Amazon, guiding them to develop solutions for forecasting, customer journey, marketing, and sustainability. Her expertise includes designing and analyzing randomized experiments, developing innovative techniques for impact measurement using observational data, combining concepts from machine learning and statistics, forecasting, and mentoring data science teams.

Education

• MS in Statistics from Stanford University
• M.Sc. in Mathematics from Birla Institute of Technology and Science, Pilani
• B.E. in Chemical Engineering from Birla Institute of Technology and Science, Pilani