Summary
Elena Goicoechea is a senior data scientist with nine years’ experience applying statistical and computational methods to clinical and public health research, currently based at the University of Chicago Biological Sciences Division. She brings deep expertise in unstructured data (text, images, time series, complex cohorts), end-to-end predictive pipelines, and causal inference to improve study design and treatment-effect estimation in mental health and neurology. Previously she led research data science at Johns Hopkins Bloomberg School of Public Health and has a strong quantitative foundation from MS work in Computational Analysis & Public Policy at UChicago and econometrics training at ITAM and the Stockholm School of Economics. Elena combines research rigor with operational focus—translating methodological advances into production-ready, interpretable implementations that health researchers can actually use. An economist-turned-data scientist, she also has experience in policy and monetary research that sharpens her ability to frame health questions within broader population and systems contexts.
9 years of coding experience
6 years of employment as a software developer
Harris School of Public Policy at the University of Chicago
Bachelor's degree, Economic Theory, Bachelor's degree, Economic Theory at Instituto Tecnológico Autónomo de México / ITAM
Econometrics and Quantitative Economics, Econometrics and Quantitative Economics at Stockholm School of Economics
Master of Science - MS, Computational Analysis & Public Policy, Master of Science - MS, Computational Analysis & Public Policy at University of Chicago
Spanish, French, English, German