Andrés Nigenda’s expertise lies at the intersection of data science and program evaluation. Their current work focuses on evaluating the federal government’s efforts to support research and education in science, technology, engineering, and mathematics (STEM). Nigenda is skilled in wrangling (un)structured data, creating data pipelines, and translating complex policy requirements into technical implementations. Nigenda’s toolkit encompasses methods such as natural language processing, data visualization, machine learning, and quasi-experimental design, which he applies to support innovative solutions for clients that include the National Science Foundation (NSF), the U.S. Department of Health and Human Services, and the Lumina Foundation.
Currently, Nigenda leads data analysis and reporting tasks for a pilot data collection system, the Education and Training Application, designed to support NSF’s human capital development programs. In other recent work, he implemented a machine learning model that cuts human screening efforts for a literature review in half and used text mining to identify and retrieve data from conference proposals to investigate the effect of NSF’s augmented anti-harassment policy.
Before joining Mathematica in 2020, Nigenda earned an M.S. in computational analysis and public policy from the University of Chicago and a B.A. in economics from the Instituto Tecnológico Autónomo de México. Their prior experience includes analyzing changes in the frequency and nature of LGBTQ+ resources across federal government domains, contributing to the impact evaluation of federal social programs in Mexico, and planning the allocation of resources for Mexico’s Social Security Institute. Nigenda has also conducted research on the effects of skilled-biased technical change on wage inequality in Mexico.