Claire Kelley

Claire Kelley

Senior Data Scientist, New Haven, CT

Education & Certification

BS, Statistics, Yale University, MA, Quantitative Methods, Columbia University

Claire Kelley is a senior data scientist at Child Trends, where she conducts and supports research across all program areas. Her primary research interests focus on the intersection of machine learning and social science, particularly in the domains of health and education.

In her work, Claire blends traditional quantitative methods with machine learning and software engineering. Some of her recent projects include using computer vision to assess bias in news articles, co-authoring an open source software package for fitting mixed effects models with complex survey weights, and creating an interactive JavaScript-based data story about algebra enrollment. In addition to writing software and conducting research, Claire is passionate about creating a community of practice around data science for social research. She regularly presents her research and teaches professional development courses at data science and social science conferences including PyData, the American Association for Public Opinion Research, and the American Education Research Association.

Prior to joining Child Trends, Claire worked as a data scientist at the American Institutes for Research (AIR) and at Merck. At AIR, Claire worked on a variety of data engineering and data science projects and led the data visualization and reporting working group. At Merck, she worked on e-commerce data science, including developing a parallelized system for generating product recommendations and using time series models for forecasting product demand. She holds a bachelor’s degree in statistics from Yale University and a master’s degree in quantitative methods from Columbia University.