1. Proficient in predictive modelling (Linear Regression, Logistic Regression, Gradient Boosting, Adaptive Boosting, Random Forest, CART, ARIMA model, k nearest neighbour ,etc.) to support business analysis 2. Adept at quantitative techniques application (GLM, variable selection, cluster analysis, PCA, classification analysis, decision tree, neural network, hypothesis test, etc..) 3. Skilled in quantitative financial modeling (the Multi-Period Binomial Option Pricing Model, the BSM Model, the One-Period/Multi-period Binomial HJM Model, the HJM Libor Model, the Risk Management Models, etc.) 4. Familiar with data analytics utilizing SAS,, R, Python and big data tools (Hadoop, Spark, AWS, etc)

Sr Data Scientist @ From April 2015 to Present (9 months) Sr Statistician @ 1. Built valuation models to support credit policy decisions (application approval, line assignment and line increase) across Private Label and Cobrand partnership credit cards by predicting key drivers such as Outstanding, Purchase Volume, Account Status, PBad and Charge Off Severity for the next 99 statements. 2. Utilized customer segments strategies, spline terms and constrained regression methodologies to capture customer behaviours over time 3. Increased risk model performance by 43% by incorporating alternative data source, creating new features, scorecard calibration, and adopting Gentle Adaptive Boosting Model 4. Prepared the modeling datasets and performed data cleaning and checking data consistency 5. Analysed customer behavior and assisted creating customer segments with data mining techniques including treenet, cart and cluster analysis 6. Validated Model performance based on the granular segments defined by business partners 7. Documented ad hoc analysis, model design and model performance 8.First Place in Capital One Risk Modeling Competition From October 2014 to Present (1 year 3 months) Washington D.C. Metro AreaQuantitative Analyst @ 1. Worked with team members and collaborates with business partners to to design, develop, implement, and monitor quantitative models to meet the business requirements, predicted likely customer behaviors (customer lifetime value, risk of attrition, product penetration etc) using SAS, SQL and Teradata 2. Combined data from multiple sources and reformat to produce analysis datasets. 3. Analyzed and interpreted collected data about customer demographics, preference and needs data to identify potential markets and factors influencing the demands for life insurance products 4. Partner with other analytics team to design and build marketing dashboards that visualize the key performance indicators 5. Write reports (Excel Pivot Tables, Tableau, Datameer) that include effective graphs, tables, summaries, and narratives to deliver research results From July 2013 to October 2014 (1 year 4 months) Charlotte, North Carolina AreaAnalyst @ 1. Reviewed data to verify integrity and consistency for completeness, accuracy and suitability, including data coding integrity, reliability of collection and interpretation. 2. Time series and forecasting analysis of the Spurs games attendance rate From January 2013 to May 2013 (5 months) Intern @ 1. Offered guidance and analysis to over 50 clients for building their personal finance budgets 2. Assisted managers to review applications, communicate with clients and make presentations From August 2011 to September 2011 (2 months) Greater Salt Lake City AreaIntern @ 1. Interacted with customers to provide information about products and services, and to handle complaints 2. Completed a research project for the agent’s digital signature vendor, which reduces operational cost by 15% From June 2011 to August 2011 (3 months) Greater Salt Lake City AreaClass President @ 1. Planned and organized activitiesto unite the 136 students of Economics School of Nankai University From September 2009 to August 2010 (1 year)

Master's degree, Applied Statistics @ Cornell University From 2012 to 2013 BA, Economics, 3.8 @ Nankai University From 2008 to 2012 BA, Economics, 3.8 @ Westminster College From 2008 to 2012 Xin Wang is skilled in: SAS programming, R, Microsoft Office, SPSS, Analytics, Databases, Data Analysis, Data Mining, Data Modeling, Economics, Financial Analysis, Financial Modeling, Risk Management, Econometrics, Python