Debasmita Das

Background

About Debasmita Das

Passionate economist with a keen interest in leveraging advanced econometrics and statistical machine learning methodologies to extract actionable insights from large and complex datasets. Throughout my research career, I conducted quantitative research using tools from causal inference, quasi-experimental methods, and ML methods. I have developed proficiency in programming languages such as Python, R, MATLAB, STATA and SQL, and have also gained multi-year experience with big data tools, cloud technologies (AWS, GCP, Azure), Linux OS environment, bash shell scripting, high-performance computing. I have extensive experience in developing and implementing cutting-edge algorithms and models to solve complex problems at the intersection of Household and Consumer Finance, Labor Economics, and Public Policy. My research has examined the effect of work requirement rules for welfare benefits, dynamics of retirement decisions of American workers, explainability of credit risk models, and a variety of other topics. I earned my PhD in Economics from Daniels School of Business, Purdue University. Currently, I am working as an Economist/Scientist II at Keystone AI. Before joining Keystone, I was a Post-doctoral Research Fellow affiliated with the Financial Services Innovation Lab in the Scheller College of Business at Georgia Tech.