Jiang Li
Executive Director at Exelixis
Short Hills, New Jersey, United States
Summary
🤩
Rockstar🎓
Top SchoolJiang Li is an Executive Director and cancer bioinformatics Ph.D. with nine years of professional experience leading statistical and data science teams across top biopharma companies, now based in Short Hills, New Jersey. He progressed through quantitative roles at Bristol-Myers Squibb, Roche, and Novartis before directing analytics at BeiGene and now guiding strategy and execution at Exelixis. Trained in biostatistics, he blends rigorous clinical-statistical expertise with hands-on machine learning — contributing practical implementations like XGBoost, custom k-means, and cross-validation utilities to the popular TheAlgorithms/R repository. Known for translating complex omics and clinical data into actionable insights, he bridges research and product development to accelerate oncology programs. Colleagues describe him as a pragmatic leader who pairs deep methodological skill with a knack for shipping reproducible, production-ready analyses.
9 years of coding experience
20 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Biostatistics, Doctor of Philosophy (Ph.D.) Biostatistics at Boston University
Github Skills (13)
data-manipulation10
algorithms10
regression10
machine-learning10
clustering10
data-science10
classification10
r10
xgboost9
neural-network8
preprocessing7
preprocess7
data-mining7
Programming languages (2)
Github contributions (5)
TheAlgorithms/R
Jan 2019 - Oct 2020
Collection of various algorithms implemented in R.
Role in this project:
Data Scientist
Data ScientistContributions:14 commits, 15 PRs, 1 push in 1 year 9 months
Contributions summary:Jiang contributed several R scripts focused on machine learning and data manipulation within the repository of R algorithms. They developed and implemented an XGBoost model for classification using the diamonds dataset, along with a custom k-means clustering algorithm. Additional contributions include linear regression, Apriori algorithm implementation, artificial neural network, one-hot encoding, LASSO regression, data normalization and standardization functions, and a k-fold cross-validation implementation, showing a diverse range of data science techniques.
regressionr-programmingdata-miningstatistical-learningmachine-learning
Ronlee12355/Road2R
Mar 2019 - Dec 2022
A curated list of amazingly awesome R libraries, resources and package collection.
Contributions:53 commits, 14 PRs, 58 pushes in 3 years 9 months
r-packagehypothesis-testingdata-preprocessingdata-sciencerstats
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