Data Science
Gaining insights to deliver meaningful social interactions
Data scientists at Facebook conduct large-scale, global, quantitative research to gain deeper insights into how people interact with each other and the world around them.
Our findings directly inform decisions to improve people’s everyday experiences on Facebook, make it easier and more intuitive to use, and find ways to facilitate meaningful social interactions. Research efforts span a variety of disciplines, including computational social science, econometrics, operations research, market intelligence, survey science, and statistical computing. We employ a mixture of methods to accomplish our goals, including machine learning, field experiments, surveys, and information visualization. We also build scalable platforms for the collection, management, and analysis of data, and actively contribute our scientific findings to the academic research community.
For information about Core Data Science research at Facebook, visit the Core Data Science page.
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Latest Publications
All PublicationsInnovative Technology at the Interface of Finance and Operations - March 31, 2021
Market Equilibrium Models in Large-Scale Internet Markets
Christian Kroer, Nicolas E. Stier-Moses
March 8, 2021
Diverse and inclusive representation in online advertising: An exploration of the current landscape and people’s expectations
Fernanda de Lima Alcantara
JMLR - February 11, 2021
The Decoupled Extended Kalman Filter for Dynamic Exponential-Family Factorization Models
Carlos A. Gómez-Uribe, Brian Karrer
EC - December 23, 2020
Matching Algorithms for Blood Donation
Duncan C. McElfresh, Christian Kroer, Sergey Pupyrev, Eric Sodomka, Karthik Abinav Sankararaman, Zack Chauvin, Neil Dexter, John P. Dickerson
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Videos
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Core Data Science at Facebook
3:09 | April 23, 2020
Open Source Projects
1:36 | August 16, 2019
Social Connectedness Index
1:11 | July 1, 2019
Social Good Projects
1:24 | July 1, 2019
Open Research Awards
View All Open Research AwardsRequest for proposals on sample-efficient sequential Bayesian decision making
With this RFP, we hope to deepen our ties to the academic research community by seeking out innovative ideas and applications of Bayesian optimization that further advance the field. We are committed to open source and will help awardees make the products of this RFP available to the public as part of BoTorch.