Programming Language: Python, SQL, R
Tool: Tableau, Looker, Excel, Google BigQuery
Charles Schwab San Francisco, CA
Business Analyst Intern September 2017 – Present
Pull raw data from Teradata data warehouse and clean, analyze, structure data with SQL.
Achieve 100% high-quality rate by developing ETL data pipeline template among 3 departments and Python to track monthly/seasonal trends and week-of-week performance of ~30 product metrics.
Slash payroll administration costs 30% by designing and building Retail Dashboard for C-Suite with Tableau, and integrating analytics to inform strategic business decisions.
Socialize with 12 business partners to identify business opportunities, conduct analysis to understand end-user behavior and select 26 key metrics in 10 dimensions for the final dashboard.
Lenovo Group Ltd. Beijing, China
Business Analyst Intern March 2016 – December 2016
Discovered strong correlation between click-through-rate (CTR) and timing, and identified potential marketing opportunity by leveraging Tableau and Python to perform exploratory data analysis(EDA).
Improved CTR by 5% by working with marketing manager on selecting target channels and timing to optimize marketing plan based on funnel analysis and A/B testing.
Created SQL templates in Redshift and performed ad-hoc analysis to support 7 business partners in their decision making and worked with Finance department and generated monthly salary reports for over 50,000 employees.
Ernst & Young Beijing, China
Analyst Intern January 2016 – February 2016
Analyzed the robustness of spreadsheets and gathered information from senior client staff to provide requested supporting documents for auditing purposes and solutions for the questionable areas.
Apple Store Monetization Experiment Design November, 2017
Improved conversion rate 3 times by identifying potential monetization opportunity. Discovered strong correlation between user conversion and payment methods through exploratory analysis on longitudinal user data with Python.
Measured impact of A/B testing in store purchase flow on sample size of 4,8000 by drawing confidence interval and calculating statistical significance.
Made recommendations of running experiment to incentivize user to purchase gift card, presented this recommendation and demoed Python Jupyter Notebook to audience of 50 people including 4 capstone committee members.
Zillow’s Home Value Prediction October, 2017
Transformed 303 features, scaled label column and normalized numerical features to apply classifiers of regression.
Used ridge regression and cross validation in Sklearn to acquire the best hyper-parameter, the number of classifiers.
Fitted linear regression model, built random forest and XGBoost relatively to predict the most accurate housing price, and achieved RMSE 0.127 on the test data set.
UC Davis Bookstore Analytics Competition September, 2017
Won the first prize and saved $7.4 million in cash flow, $1.48 million in actual money lost and 8250 extra man hours.
Leveraged Python to do data scraping and get 18,000 books’ lowest price on Amazon using Amazon Product API.
Preprocessed missing values and outliers based on EDA results, performed data transformation and data type conversion on dataset (25 columns*18,000 rows) with Python Pandas.
University of California, Davis San Francisco, CA
Master of Science in Business Analytics 2017 – 2018
Coursework: Data Management, Advanced Statistics, Data Visualization, Big Data, Machine Learning
Renmin University of China Beijing, China
Bachelor’s Degree, Economics & Journalism 2013 – 2017
Coursework: Probability and Statistics, Econometrics, Industrial Organization