Ken Larrey
Data Science Specialist
Greater Boston United States
Summary
👤
Senior🎓
Top SchoolKen Larrey is a Data Science Specialist at McKinsey with 11 years of experience applying analytical rigor to real-world business problems and shipping production-ready ML solutions. Trained in both electrical engineering and economics at Duke, he blends quantitative modeling, software engineering, and product-minded pragmatism to translate complex data into actionable insight. At McKinsey he has progressed from advisor to specialist, pairing client-facing strategy work with hands-on implementation. He’s also contributed to the popular Gensim project—making word2vec deterministic and more reproducible—showing a commitment to dependable, research-grade tooling. Based in Greater Boston, Ken thrives at the intersection of reproducible ML research and enterprise delivery.
11 years of coding experience
1 year of employment as a software developer
Bachelor of Science (B.S.), Electrical and Electronics Engineering, Bachelor of Science (B.S.), Electrical and Electronics Engineering at Duke UniversityHigh School, High School at St. John's School, Houston TX
Github Skills (7)
word-embeddings10
gensim10
topic-modeling10
nlp10
python10
data-science9
machine-learning9
Programming languages (2)
Github contributions (5)
piskvorky/gensim
Sep 2014 - May 2015
Topic Modelling for Humans
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 2 PRs, 16 comments in 7 months
Contributions summary:Ken primarily focused on improving the Gensim library's word2vec implementation. Their contributions centered around introducing deterministic initialization for word vectors, allowing for reproducible results across different training runs and datasets. They also refactored the code to use built-in hash functions instead of external libraries and made minor comment and spacing adjustments, and removed obsolete code parameters.
pythonword-similarityword-embeddingsdata-miningfor-humans
KCzar/BulkFindReplace
Oct 2016 - Oct 2016
Contributions:4 pushes, 1 branch, 1 tag in 1 day
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