Nate Jones
Staff Data Scientist & MLE
Chicago, Illinois, United States
Summary
👤
Senior🎓
Top SchoolNate Jones is a Staff Data Scientist and Machine Learning Engineer with 8 years of experience building end-to-end ML systems across computer vision, NLP, tabular data, and recommendation engines. He’s currently leading development of a universal embedding model for video, image, and text at LTK, after previously shipping low-latency on-device vision models and a personalized brand recommender that boosted CTR by over 68%. Nate has a strong track record of productionizing models and libraries—open-sourcing a deep learning recommendations library at ShopRunner and deploying transformer-based and unsupervised fraud models in production. He pairs academic training (MS in Data Science, BS in Computer Science) with hands-on cloud and deployment experience, often working solo to architect solutions for bias detection and automated content analysis. Notably, his projects repeatedly translate into measurable business impact (e.g., 184% email lift, 55% CTR gains, significant chargeback reductions), and outside work he’s an unusually dedicated Taco Bell aficionado.
9 years of coding experience
8 years of employment as a software developer
Master's degree Data Science, Master's degree Data Science at Illinois Institute of Technology
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (56)
pytorch10
artificial-intelligence10
recommendation-system10
python10
data-science10
machine-learning10
implicit10
deep-learning10
ai10
recommender-system10
data-visualization10
streamlit10
image-text10
developer-tools10
data-analysis10
Programming languages (7)
Github contributions (5)
the-mom-project/collie
Jan 2022 - Mar 2022
A library for preparing, training, and evaluating scalable deep learning hybrid recommender systems using PyTorch.
Contributions:50 reviews, 104 comments in 2 months
pytorchscalablehybrid-recommenderrecommenderhybrid-learning
ShopRunner/collie
Apr 2021 - Sep 2022
A library for preparing, training, and evaluating scalable deep learning hybrid recommender systems using PyTorch.
Contributions:87 reviews, 139 commits, 36 PRs in 1 year 5 months
deep-learningpytorchrecommender-systemdatasciencehybrid
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