Jerry Wang
Principal at Boston Consulting Group (BCG)
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolJerry Wang is a Principal at BCG and BCG X in Boston with a decade of experience translating advanced data science into strategic impact across consulting engagements. Trained in data science, applied economics, and business (Columbia, Georgetown, Quantic), he combines rigorous quantitative modeling with commercial judgment to lead cross-functional teams from prototype to scaled solutions. He has progressed rapidly through technical and leadership roles at BCG, moving from analytics associate to principal while guiding projects as a lead data scientist and project leader. An active ML engineer in open source, he contributed feature-importance and SHAP visualization capabilities to the well-regarded NGBoost probabilistic boosting library, highlighting his focus on interpretable, production-ready models.
10 years of coding experience
6 years of employment as a software developer
Master of Business Administration - MBA, Master of Business Administration - MBA at Quantic School of Business and TechnologyMaster of Arts (M.A.) Applied Economics, Master of Arts (M.A.) Applied Economics at Georgetown UniversityBachelor of Science (B.S.) Biological Sciences Economics, Bachelor of Science (B.S.) Biological Sciences Economics at Fudan UniversityMaster of Science (M.S.) Data Science, Master of Science (M.S.) Data Science at Columbia University
Chinese, English
Stackoverflow
184reputation
15kreached
3answers
2questions
Github Skills (17)
shap10
python10
feature-selection10
scikit10
machine-learning10
gradient-boosting10
scikit-learn10
jupyter-notebook9
uncertainty8
estimate8
mathematical-optimization6
h2o6
roc6
derivative6
xgboost6
Programming languages (8)
Github contributions (5)
stanfordmlgroup/ngboost
Jan 2020 - Jan 2020
Natural Gradient Boosting for Probabilistic Prediction
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 4 PRs, 5 comments in 27 days
Contributions summary:Jerry implemented and illustrated feature importance functionalities for the NGBoost model. This involved adding a new property for calculating feature importances, incorporating it into the core `ngboost.py` file. The user also created and enhanced demonstration notebooks (e.g., "Feature importance.ipynb" and "SHAP plots.ipynb") that showcase the model's interpretability through SHAP plots, and demonstrate how to visualize feature importance.
pythonpredictionnaive-bayesnatural-gradientsmachine-learning
zhiruiwang/Talk_at_CAS
Jan 2018 - Feb 2018
Contributions:40 pushes, 1 branch in 1 month
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.