Dan Sinh Nguyen

CONTACT

INFORMATION

Location: Seattle, WA LinkedIn: www.linkedin.com/in/dan-sinh-nguyen Telephone: 765-***-**** GitHub: https://github.com/dansinh Email: ac6yh6@r.postjobfree.com Website: sinhnguyen.squarespace.com EDUCATION Ph.D., Political Science, Purdue University, Indiana May 2018 M.A., International Peace Studies, University of Notre Dame, Indiana 2009 B.A., International Relations, Institute for International Relations, Hanoi 2003 SKILLS Programming: R, Python (Numpy, Pandas, Matplotlib, Scikit-Learn), SQL, Stan. Analysis: Causal Inference, Statistical Modeling, Regression, Experimental Design, Survey and Sampling.

Machine Learning: Random Forest, Extreme Gradient Boosting, Super Learner, PCA. Tools: AWS, GitHub, NVivo, Stata, LATEX.

EXPERIENCE Data Science Fellow, Insight Data Science, Seattle, WA June 2018–present

Developed a data pipeline and conducted exploratory data analysis of 10M+ data points on app usage behaviors and tracking data.

Built causal models to estimate the causal impact of app usage and used ensemble machine learning models to predict user engagement for a startup company. Graduate Instructor, Purdue University, West Lafayette, IN 2011–2018

Taught 10 undergraduate courses in international relations and statistical methods for political analysis using the R language.

Graduate Researcher, Purdue University, West Lafayette, IN 2009–2018

Employed XGBoost and Super Learner with directed acyclic graphs (DAGs) of causal models to investigate the causes, causal mechanisms, and consequences of human rights treaties.

Used the NLP NVivo software to analyze 1K+ human rights texts from Amnesty In- ternational to identify cases of torture and better understand NGOs and government behaviors.

Research Intern, Stimson Center, Washington D.C. 2008

Conducted qualitative research on the environmental and social impacts of dam con- struction in Southeast Asia.

Wrote spotlight reviews and commentaries for think-tank audiences. QUANTITATIVE

COURSEWORK

Graduate coursework in Department of Statistics

Probability Theory; Statistical Inference; Applied Regression Analysis; Design of Exper- iment; Survey and Sampling Techniques; Advanced Statistical Methodology; Program- ming in R; Bayesian Data Analysis; Introduction to Computational Statistics. Graduate coursework in Department of Computer Science Advanced Machine Learning.