I am a Ph.D. candidate at the Joint Program in Survey Methodology and Data Science (U. of Maryland), a Data Scientist II at NORC at the University of Chicago, and an Adjunct Professor at PUCP Economics Department and the AI Lab for Public Policy. 

I hold an M.Sc. in Computational Analysis and Public Policy from the University of Chicago, where I graduated with honors as a recipient of a full scholarship from the Peruvian Government.  

I earned both my Licentiate and Bachelor's degrees in Economics with first-class honors from Pontificia Universidad Católica del Perú. 

My expertise lies in applying advanced statistical and machine-learning techniques to solve real-world challenges. This includes rigorous survey methods, causal inference techniques, and collecting data through effective methodologies. 

With 10+ years of experience in research and teaching, I have worked across academia, research institutions, multilateral organizations, and public offices, developing a deep expertise in data-driven policy analysis and research. My previous roles include:

• Academia: Teaching and Research Assistant at GSU, PUCP, UARM, GRADE, IEP

• Private Research Organizations: Peruvian Bank Association, MSI, DRS

• Multilateral Organizations: UNDP,  World Bank,  IRD France, ILO, UNFPA, UNESCO 

• Public institutions: INEI, MIDIS, MINEDU 

I am a Technical Advisor for the Peruvian National Statistical Office and the National School of Statistics. 

My interests include data science, surveys, microeconometrics, causal inference, and public policy. I am passionate about leveraging quantitative methods to drive informed decision-making and policy improvements. 

Feel free to reach out at: acozzubo [at] umd [dot] edu, or find me on Twitter.