Sandeep Shetty
Sandeep Shetty is a senior data scientist in the Data Science and Advanced Analytics group program at AIR. He is an applied labor economist and an econometrician with experience in program evaluation, policy simulation, machine learning, text mining, and analysis. He has extensive experience in working with large datasets (structured and unstructured) using statistical, econometric, and machine learning methods in R, Python, Stata, and SQL.
Dr. Shetty is currently working on a social media listening project for the FDA’s Center of Tobacco Policy. In the past, for the FDA, he has supported literature reviews on methods and tools on agent-based models and simulation, graph-based social network analysis, and their potential applications for tobacco policies. He has also supported analysis of tobacco retailer inspections data.
For a U.S. Department of Labor (DOL)-funded contract on Worker Leave Analysis and Simulation, Dr. Shetty led an administrative cost financial module as well as conducted empirical analysis of paid leave and Family Medical Leave Act policy using FMLA 2000 and 2012 survey data on topics such as maternity leave usage; leave-taking among older workers; caregiving responsibilities; and awareness and the extent of coverage and usage of FMLA-type leaves.
Dr. Shetty also led a regulatory analysis project for the Wage and Hour Division at DOL, where he implemented a text analytics pipeline to extract, store, analyze, and report on public comments received on federal regulations. He conducted ad-hoc analysis in support of the regulation drafting process. He has worked on international projects, supporting and leading various tasks as part of the experimental evaluation of a child-labor mitigation program, Bal Mitra Gram (Child Friendly Village), in India for the DOL-ILAB, and livelihood services evaluation study in Myanmar. He led a study for the DOL on Employer’s View on Short-time compensation, which was a synthesis of an employer survey implemented in four states to understand employer perceptions of and experience with the policy.