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Mamadou S. Diallo

Pronouns: He/Him

Principal Data Scientist
Mamadou is an esteemed international data scientist with expertise in building statistical software and data systems.

As principal data scientist in NORC’s Statistics & Data Science department, Mamadou develops statistical software using open-source programming languages such as Python and R. He has extensive international experience providing technical support and capacity building to data teams from countries in Africa, Middle East, and Asia. He conducts research in small area estimation and machine learning methods for producing disaggregated and real-time data.

Before joining NORC, Mamadou worked for more than six years as the global lead for immunization data at the United Nations Children’s Fund (UNICEF), where he led the WHO-UNICEF Estimates of National Immunization Coverage (WUENIC) funded by GAVI, the Vaccine Alliance and its partners. The WUENIC publishes annual historical times-series (1980-present) data for 14 vaccine doses for 195 UNICEF member states. He provided technical support to dozens of UNICEF member states by training cohorts of statisticians, writing guidelines, designing monitoring and evaluation studies, supporting statistical analyses, and building data collection tools and dashboards.

As lead statistician and operations manager at the Saudi Center for Opinion Polling (SCOP), he led the design and analysis of dozens of CATI surveys on the Saudi Society and oversaw the operations of the SCOP call center. As senior statistician at Westat, he supported many national and international surveys such as the National Health and Nutrition Examination Survey (NHANES), the National Crime Victimization Survey (NVCS), and the Population-based Health Impact Assessment (PHIA). With Robert E. Fay, they developed a small area estimation model to estimate crime rates at the state and county levels for the Bureau of Justice Statistics (BJS). He started his career at Statistics Canada as a mathematical statistician.

Mamadou has published many peer-reviewed papers on small area estimation, machine learning techniques, and related topics. He is the creator and maintainer of one of the reference Python package samplics for designing and analyzing complex survey data.