Taylor Pospisil
Staff Research Data Scientist
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolTaylor Pospisil is a Staff Research Data Scientist with 11 years of experience applying statistical rigor and machine learning to complex product problems, currently focused on detecting and understanding generative content at YouTube. A Carnegie Mellon PhD in Statistics and Data Science, Taylor spent six years at Google developing north star metrics, calibrated models, and the first measurements of user sentiment toward ads, plus a modular framework for long-term revenue estimation that accounted for feedback loops. Comfortable bridging research and product, they have a track record of shipping analysis-driven launches and building reusable tooling (e.g., a concise Python DSL during an earlier internship). Based in Mountain View, Taylor combines deep probabilistic modeling and practical systems design, with a background in optimizing density-estimation losses for multimodal prediction that informs robust, production-ready approaches to generative content detection.
11 years of coding experience
6 years of employment as a software developer
Bachelor's degree, Statistics, Math, Bachelor's degree, Statistics, Math at Duke UniversityDoctor of Philosophy - PhD, Statistics and Data Science, Doctor of Philosophy - PhD, Statistics and Data Science at Carnegie Mellon University
Github Skills (28)
hierarchical9
density-estimation9
data-storage7
statistics6
machine-learning6
julia5
kernel5
gadfly5
linear-regression2
regularization2
r-package2
classification2
neural-network1
pdf1
pdf-files1
Programming languages (5)
Github contributions (5)
minimallysufficient/minimallysufficient.github.io
Jan 2015 - Oct 2018
Contributions:83 commits, 25 issues in 3 years 10 months
lee-group-cmu/RFCDE
Feb 2018 - Aug 2019
Random Forests for Conditional Density Estimation
Contributions:1 release, 64 commits, 38 pushes in 1 year 6 months
conditionalmachine-learningdensity-estimationestimationforests
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