With over 10 years of experience in both industry and academia, I specialize in designing actionable AI and data science workflows that turn complex data into strategic advantage.
The goal of Deep Strategies is to help businesses leverage AI, predictive analytics, and automation to streamline operations, uncover hidden opportunities, and drive better decision-making.
Previously, I worked as a Senior Data Scientist at a leading S&P 500 company, where I built AI and machine learning models that shaped large-scale business strategies.
I hold a PhD in sociology from the University of Wisconsin-Madison and completed my postdoctoral training in computational demography at the University of California, Berkeley. My academic research examines how economic change shapes social and demographic processes and has been published in journals such as Social Forces, Demography, Population and Environment, and SSM – Population Health.
My research has been covered by The New York Times and The Washington Post and I had the opportunity in 2020 to speak at a United Nations expert group meeting on the demographic consequences of Covid-19.
Prior to my advanced academic training, I received a BA in Sociology and French from Tulane University and a MA in Applied Quantitative Research from New York University.
“Cohort-Specific
Experiences of Industrial Decline and Intergenerational Income
Mobility”
(Social Forces, June 2024)
“Sociodemographic
characteristics alone cannot predict individual-level
longevity”
with Casey Breen (Working Paper, July 2023)
“The
Intergenerational Consequences of Climate Change on Later Life
Mortality: Evidence from Individual-Level U.S. Death Records”
(Working Paper, May 2021)
“The
Economic Underpinnings of the Drug Epidemic”
(SSM – Population Health, December 2020)
“Unequally
Insecure: Rising Black/White Disparities in Job Displacement,
1981-2017”
with Elizabeth Wrigley-Field (Working Paper, February 2020)
Nathan Seltzer, nathan.seltzer@nyu.edu, 2020