Yannet Interian
Principal ML Scientist at Esade
San Francisco, California, United States
Summary
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Rockstar🎓
Top SchoolYannet Interian is a data science and AI consultant and academic with 12 years of experience bridging applied research and product-facing analytics in the Bay Area. She holds a Ph.D. in Applied Mathematics from Cornell and has taught machine learning and deep learning at the University of San Francisco, developing curricula from exploratory data analysis to reproducible statistical workflows. Her industry work spans Google (YouTube, Google+), startups, and a co-founded company where she built production ML tooling in Go and large-scale feature pipelines on Hadoop/MapReduce. Yannet combines rigorous statistical modeling—user behavior, ad quality, recommendation systems—with hands-on engineering to deliver scalable, production-ready solutions. She frequently moves between consulting and academia, currently holding visiting and independent consulting roles in San Francisco and Barcelona, and is known for translating early signals into reliable predictive models. An uncommon strength is her track record of shipping both research-grade courses and production ML systems, making her effective at mentoring teams and shaping data strategy.
13 years of coding experience
18 years of employment as a software developer
Ph.D Applied Mathematics, Ph.D Applied Mathematics at Cornell UniversityBs in Mathematics, Bs in Mathematics at University of Havana
Italian, Spanish
Github Skills (20)
pytorch10
python10
machine-learning10
fastai10
deep-learning10
gpu10
data-acquisition8
visualization7
exploratory-data-analysis7
matplotlib6
oauth5
eda5
pandas5
rails4
ruby4
Programming languages (3)
Github contributions (5)
USF-ML2/notebooks
Jan 2018 - Sep 2018
Contributions:30 commits, 24 pushes, 1 branch in 8 months
embeddings
yanneta/msds692
Apr 2023 - Oct 2024
MSDS692 Data acquisition
Contributions:171 pushes, 1 branch in 1 year 5 months
data-acquisition
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