Drew Conway
Managing Director at Tribe AI
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolDrew Conway is a data science leader and entrepreneur with 16 years of experience building analytic products and teams across startups, academia, government, and Fortune 100 firms. As Managing Director and Head of Data Science for Private Investing at Two Sigma, he designs data-driven tools for real estate, private equity, and venture capital while advising AI startups and academic programs. He co-founded DataKind and founded Alluvium (acquired), demonstrating a track record of turning research-grade methods into practical, mission-driven products. Drew’s background blends a PhD in politics and early computational social science work with hands-on data engineering and modeling—his contributions include machine learning code tied to the influential "Machine Learning for Hackers" corpus. He frequently lectures and serves on advisory boards, bringing a rare mix of scholarly rigor, operational leadership, and product-focused intuition to complex data problems. Based in New York, he’s known for bridging the gap between social science questions and scalable, production-ready analytics.
16 years of coding experience
13 years of employment as a software developer
PhD Politics, PhD Politics at New York UniversityBA Computer Science and Political Science, BA Computer Science and Political Science at Hamilton College
Stackoverflow
5,447reputation
534kreached
16answers
12questions
r
top-5%
igraph
top-5%
Github Skills (18)
data-visualizations10
r10
machine-learning10
data-visualisation10
data-visualization10
ggplot10
data-analysis10
classification9
webscraping9
igraph9
text-mining8
naive-bayes8
social-graph6
word-wrap6
social-networking6
Programming languages (2)
Github contributions (5)
johnmyleswhite/ML_for_Hackers
Feb 2012 - Dec 2013
Code accompanying the book "Machine Learning for Hackers"
Role in this project:
Data Scientist
Data ScientistContributions:23 commits, 1 issue in 1 year 10 months
Contributions summary:Drew contributed code primarily focused on implementing machine learning techniques within the context of a book on the topic. The commits involve loading, cleaning, and analyzing data, particularly UFO sighting data. The user demonstrates skills in data manipulation, visualization, and statistical analysis using the R programming language and libraries like ggplot2 and plyr. The code suggests the implementation of a classification model for spam detection using the tm library for text mining and naive Bayes, as well as creating a recommendation system.
pythondata-sciencemachine-learning-bookmachine-learninghackers
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