XIN (REBECCA) WANG

adtq5h@r.postjobfree.com https://github.com/xuw135/Projects.git Los Angeles, CA, 90015 814-***-**** QUALIFICATIONS

Programming Languages: Python, R-Studio, SQL, Linux, SAS, C++ Data Visualization: Tableau, Power BI, D3js, Python (seaborn, matplotlib), Elasticsearch and Kibana Databases: MySQL, MongoDB

Frameworks: Spark, Hadoop, Hive, Map Reduce, Kafka Applications: SPSS, Stata, Minitab

Highlights: Big Data, A/B Testing, Bayesian Inference, Clustering, Regression, Predictive Modeling, Database Architecture, Survival Analysis, Trend Analysis, Time Series Analysis, Machine Learning: Supervised (GLM, ARIMA, Logistic, Ridge, LASSO, Bayes, SVM, Tree-based, KNN); Unsupervised

(K-means, Hierarchical clustering, PCA); Reinforcement (MDP, Dynamic programming); Deep learning (RNN, CNN, LSTM) Honors: Dean's List: Fall 2017, Spring 2018, Fall 2018, Spring 2019, Fall 2019 Extracurricular Activities: Member of Beta Alpha Psi and Olin Marketing Association, Teaching Assistant: Big Data and Cloud Computing, Database Design and SQL

EDUCATION

Washington University in St. Louis, Olin Business School, St. Louis, MO Sep. 2020-Dec. 2021 Master of Science in Business Analytics

The Pennsylvania State University, University Park, PA Sep. 2015-Dec. 2019 Bachelor of Science in Economics, Minors in Statistics and Business PROJECTS

Chatbot (Python)

• Preprocessed the data, tokenized sentences into words, lemmatized each word and remove duplicates

• Created training and testing data, built a deep neural network that has 3 layers, used Keras sequential API

• Implemented GUI and predicted the responses

Mall Customers Segmentation by Machine Learning (R)

• Analyzed and visualized customers’ demographic data

• Implemented K-means clustering algorithm to segment customers based on age, gender, annual income and spending

• Determined the optimal number of clusters and specified each cluster

• Designed marketing campaigns for each cluster of customers Movie Recommendation System (R)

• Preprocessed the data by selecting useful data, normalizing data and binarizing data

• Created matrix using one-hot encoding which comprised of corresponding genres for each movie

• Identified similarities between movies by genres and developed Item Based Collaborative Filter; recommended 10 movies watched by similar users Rocket Fuel (R)

• Split dataset into test and training sets, used logit regression to predict customer conversion rate

• Identified the relationship between impression and conversion rate, provided recommendations for the most effective range of ad impressions

• Used RFM model to give each customer a score based on their purchasing behaviors; implemented email marketing WORK EXPERIENCE

Business Analyst Intern THE BEHAVIOURAL ARCHITECTS, Shanghai, China Jun. 2021 - Dec. 2021

• Applied expertise in quantitative analysis, market research, and presentation of data to see beyond the numbers and understand home appliance market; identified opportunities to drive business growth and improve customer engagement

• Led the market research on home appliances and compared 5 major competitors to help client differentiate products in Greater China market; preprocessed data and performed data visualization (Tableau), extracted features based on density bands, established marketing strategies

• Designed and modified customer in-store experience survey, applied quantitative analysis using t-test and regressions to discover significant factors of in-store shopping experience; provided marketing campaign suggestions for Shanghai new kid’s store; used SQL to find innovations and insights

• Actively built processes and tools to make data more accessible and interpretable, effectively interacted internally with engineering teams for marketing plans and requirements, built automatic analytic models to target and connect with potential business partners

• Communicated with internal partners and external stakeholders to define appropriate data reports and dashboards that became integral to the decision-making process, influenced analytics teams through presentation of data-based recommendations, communicating state of business, experiment results, and spreading best practices

Consultant Analyst, Washington University in St. Louis SCHNUCKS MARKETS INC, St. Louis, MO Jan. 2021 - May 2021

• Worked with other student analysts and business users to translate transaction data to business insights, ensured consistent methodologies were followed and to make recommendations where necessary

• Cleaned transaction data of 10,410 customers over a 5-year period utilizing R and cloud computing tools to reorganize data into weekly segments and split into before and during Covid time periods

• Clustered 10,410 customers into 8 groups using K means clustering based on percentage of shopping to target customers

• Built machine learning models including random forest, boosted trees and regression trees in R from selected data, to develop insights on customer loyalty based on customer behavioral data and provided recommendation