Núria is a statistician in the Statistics and Data Science department at NORC at the University of Chicago. She specializes in data engineering, record linkage, and the application of statistical and machine learning models for public and social research. Her work emphasizes computational reproducibility and methodological transparency.

At NORC, Núria develops data infrastructure and methodologies to process and analyze administrative and survey data across domains such as traffic safety, higher education, and public health. She has played a key role in the National Center for Science and Engineering Statistics (NCSES) Privacy-Preserving Record Linkage (PPRL) demonstration project and the Data Linkage Program, helping to establish secure, standardized frameworks for federal data linkage efforts. Núria has also contributed to the modernization and optimization of data workflows for the Public Health Accreditation Board (PHAB) and the National Highway Traffic Safety Administration (NHTSA). Her work centers on making these systems more efficient, scalable, and open-source, while integrating external data sources and applying statistical methods and visualizations to support evidence-based decision-making.

Prior to joining NORC, Núria worked in data science consulting at Accenture, where she specialized in predictive analytics, relational database management, and data engineering pipelines. She also held fellowships with the Data Science for Social Good program at Carnegie Mellon University and the Data Science Institute at the University of Chicago, deepening her expertise in the application of data science to social impact challenges.