Natalia De Oliveira is a data scientist with eight years of experience and a freshly completed PhD in Statistics and Machine Learning from Carnegie Mellon University, where her research tackled practical estimation of predictive risk and out-of-sample error. Now at Google, she blends rigorous statistical theory with production-minded applied work, having contributed to projects from autonomous package delivery to COVID-19 modeling with CMU’s Delphi team. Her background includes industry research internships and funded academic fellowships in Brazil, where she developed novel inference methods for option pricing and significance testing. Comfortable moving between theory and applied systems, she brings deep probabilistic thinking to real-world ML problems and a track record of translating complex methodological advances into operational analytics.
8 years of coding experience
Master of Science - MS, Statistics, Master of Science - MS, Statistics at Joint Graduate Program in Statistics UFSCar/USP
Doctor of Philosophy - PhD, Statistics and Machine Learning, Doctor of Philosophy - PhD, Statistics and Machine Learning at Carnegie Mellon University
Bachelor of Science - BS, Statistics, Bachelor of Science - BS, Statistics at Universidade Federal de São Carlos
Public-facing documents and tools supporting Delphi's COVIDcast effort.
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effortsupportingdelphicovidcast
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