Isabel specializes in developing statistical and data science methods to draw insights from complex data sources. She has worked extensively with clinicians and health officials in the United States and Tanzania to improve access to quality reproductive healthcare.
As VP of Data Science, she harmonizes rich data sources, innovative data science tools, and the vast clinical expertise at Delfina to build patient-centered technologies that improve pregnancy care for all birthing parents. Isabel has a PhD in biostatistics and was formerly a postdoctoral fellow at the Harvard Data Science Initiative.
When Isabel is not wrangling data, you can find her training for a triathlon, exploring local breweries, or hanging with her cats, Boo & Nala.