Brice Green
Doctoral Student at Massachusetts Institute of Technology
Cambridge, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolBrice Green is a doctoral student and pre-doctoral researcher at MIT with nine years of experience applying economics, Bayesian statistics, and quantitative financial modeling to real-world problems. He blends academic rigor with hands-on data science—contributing to influential causal inference work (e.g., improving visualizations and robustness in the mixtape repository) and building predictive models and portfolio analytics in client-facing roles. Comfortable coding in R and managing dependencies, he has a track record of automating analytics workflows and fixing subtle statistical issues like collinearity and matching implementations. Trained in economics and music at Williams College, he brings a creative, multidisciplinary perspective to modeling and communication. Based in Cambridge, MA, he pairs deep quantitative skills with experience translating results for stakeholders, often surfacing practical fixes that improve reproducibility and interpretability.
9 years of coding experience
3 years of employment as a software developer
Bachelor of Arts (B.A.), Economics and Music, Bachelor of Arts (B.A.), Economics and Music at Williams College
English, Spanish
Github Skills (9)
ggplot10
statistical-models10
causal-inference10
r10
data-analysis10
data-visualisation9
data-visualization9
data-visualizations9
regression-analysis8
Programming languages (8)
Github contributions (5)
scunning1975/mixtape
Apr 2020 - Apr 2020
Data and Program files for Causal Inference: The Mixtape
Role in this project:
Data Scientist
Data ScientistContributions:19 commits, 3 PRs, 1 comment in 13 days
Contributions summary:Brice primarily contributes to statistical analysis and causal inference within the repository. They focused on improving the accuracy of visualizations by correctly displaying error bars, fixing colinearity issues in the code, and updating code related to matching and event study analysis. Their work involves data manipulation, statistical modeling, and the application of causal inference techniques. They also addressed issues related to package dependencies and data reading.
causal-inference
wwiecek/baggr
Jul 2019 - Feb 2020
R package for Bayesian meta-analysis models, using Stan
Contributions:44 commits, 8 PRs, 104 comments in 7 months
bayesianrr-packagemeta-analysisstan
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