Risk Analyst/Modeler

Strong Analytical Background with Experience in SAS Programming and Scorecard Model Development.

PhD candidate in Probability and Master in Statistics, solid overall theoretical background
Strong critical thinking ability and problem solving skills.
Strong SAS programming skills.


Risk Analyst, Financial Services Company Confidential, IL June 2011 - Present

  • Using SAS to develop a complete logistic regression scorecard model to predict the likelihood of payment incidence within 6 months. The model showed significant increase in forecast precision when tested against the hold out sample.
  • Selected 70 variables out of 300 by comparing the divergence, Spearman correlation, and information value.
  • Used a cluster analysis to identify 15 variables that might be used in the final model.
  • Fine grouped each variable, compared the incidence log odds of each fine group and collapsed the similar groups to achieve coarse grouping.
  • Create dummy variables to be used in the logistic regression model. Fine tuned the groupings based on the regression coefficients to correct for over fitting and achieve maximum predictive power.
  • Validate the new scorecard model on the hold out sample.
  • Evaluation of competing regression approaches for scorecard development (standard vs. weight of evidence approach).
  • By using the new scorecard model, further considered balance to predict liquidity.
  • Examined 4 different portfolios, used a linear regression to estimate the seasonality factors.
  • Using SAS to summarize the various distributions from a data set of size over 10GB.
  • Break down all the accounts in both the opportunity and cohort data sets into smaller groups by the payment incidence score and balance, compare the volume in each group to estimate the liquidity.
  • Analyzed settlement propensity.
  • Helping to train and mentor other analysts.

Research Assistant, Department of Sociology, University of Utah March 2010 - April 2011

  • Using longitudinal data from the Health and Retirement Survey, we applied SAS Proc Reg, Proc Logistic, Proc Mixed, and Proc Glimmix to examine the associations among race, ethnicity, immigrant status, and body mass index and explore how factors such as socioeconomic status and acculturation contribute to disparities in body mass across different groups defined by race/ethnicity and immigrant status.

Teaching and Research Assistant, Department of Math, Northwestern University 2002-2008


  • M.S. in Statistics, University of Utah, Salt Lake City, UT 2009 - 2011
  • M.S. in Mathematics, Ph.D. Candidate, Northwestern University, Evanston, IL 2002 - 2008
  • PhD qualify exam (oral) passed in Spring 2005 (Emphasis: Probability) Graduate student fellowship, Northwestern University, 2002-2003. Teaching assistantship, Department of Mathematics, Northwestern University, 2003-2008
  • B.S. in Mathematics, Peking University, Beijing, China 1996 - 2000
  • Certificate:  Preliminary Actuarial Examination 1/P passed May 2010


Proficient in the use of various software packages including SAS, R, SPSS, STATA.
Thorough understanding of Statistics, Probability, Markov Chains Theory, Stochastic Analysis, Martingale Theory, Coupling Method, ODEs, PDEs. Working knowledge of Multivariate Statistical Analysis, Principle Components, Factor Analysis, Survival Analysis, Time Series, Theory of Derivatives, Option Pricing Models, Hedging Strategy, Numerical Analysis.
Major strengths in planning, problem solving. Demonstrated accuracy, attention to detail and ability to work well in team environment.