Matt Taddy
VP Amazon Private Brands
Seattle, Washington, United States
Summary
👤
Senior🎓
Top SchoolMatt Taddy is a seasoned quantitative leader with 48 years of experience bridging academic econometrics and large-scale product execution, currently serving as VP for Amazon Private Brands after leading Economic Technology at Amazon. He previously held faculty rank as Professor of Econometrics and Statistics at Chicago Booth and led economics and data science teams at Microsoft and eBay, demonstrating a rare blend of rigorous research and operational leadership. His PhD in Applied Mathematics and Statistics and background in mathematical statistics underpin a career that moves comfortably between publishing novel methods and shipping production systems. An active contributor to open-source ML tooling, he has improved core Word2Vec scoring and performance in the popular gensim project, including Cython optimizations for speed. Based in Seattle, he is known for turning complex causal and probabilistic models into scalable business decisions across tech and academia.
48 years of coding experience
4 years of employment as a software developer
University of California Santa CruzMaster's degree, Mathematical Statistics, Master's degree, Mathematical Statistics at McGill University
Github Skills (10)
gensim10
word2vec10
python10
cython10
natural-language-processing9
performance-optimization9
machine-learning9
topic-modeling8
c178
c118
Programming languages (3)
Github contributions (5)
piskvorky/gensim
Mar 2015 - Feb 2016
Topic Modelling for Humans
Role in this project:
Back-end Developer
Back-end DeveloperContributions:36 commits, 6 PRs, 41 comments in 1 year
Contributions summary:Matt made multiple commits focused on improving the scoring mechanism within the word2vec model. The initial commits involved modifying and optimizing the `score_sentence_sg` function in `word2vec.py`, suggesting attempts to address performance issues. Subsequent commits involved the transition of core functionalities to Cython, specifically the generation of `word2vec_inner.c`, suggesting an effort to boost computational efficiency.
pythonword-similarityword-embeddingsdata-miningfor-humans
TaddyLab/MBA
Dec 2020 - Jan 2022
Contributions:27 commits, 94 pushes, 1 branch in 1 year
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