Ph.D. Biostatistics 2007 (Chapel Hill, NC)

Quantitative Research Analyst or Predictive Modeler

Job-related skills

• Strong academic background in business analytics, statistics, and market research
• Excellent analytical and critical thinking skills.
• Good written and verbal communications and presentation skills.
• Ability to communicate effectively on virtual teams.
• Expertise in model building, statistical algorithms, numerical analysis, computational statistics and data analysis.
• Strong programming skills in SAS, R/S-Plus or C/C++
• Strong data mining techniques
• Taken KICPA exam

Statistical Information Company, Seoul, Korea 2001
Title: Internship
Responsibility: Provided statistical expertise and leadership to project teams

• Driving in-house analysis combining both the customer database data as well as survey results in generating business insights
• Provided statistical data analysis such as regression, factor analysis, and multivariate analysis.
• Modeling customer behavior, market drivers and business forecasting based on primary and syndicated data, using SQL.
• Applied data-mining techniques such as neural networks to Pricing and Revenue Management (PRM)
• Extract and analyze market and demographic data

Korea University, Seoul, Korea
B.S. Department of Statistics, 3/1995 - 8/2002

Related classes

Statistical computing, financial econometrics, data mining, marketing, risk analysis, topics in Asset Pricing, Empirical Modeling for risk and insurance, consumer behavior, research methods in marketing, stochastic processes, and forecasting and time series analysis

Technical skills

• Basic statistical methods and advanced statistical methods such as statistical methods in clinical data analyses, survival analysis, nonlinear regression problem, (non)linear mixed models, linear regression, multivariate linear regression, logistic regression, Poisson regression, generalized linear model, nonparametric models, categorical data analysis, ANOVA, MANOVA, MANCOVA, ANOCOVA, power and sample size calculation, survey data analysis.
• SAS programming: SAS/BASE, SAS/STAT, SAS/MACRO, SAS/GRAPH, SAS/SQL, SAS/INSIGHT, and SUDANN.
• The SAS procedures being used are NPAR1WAY, PHREG, LIFEREG, LIFETEST, GLM, MIXED, NLIN, GLIMMIX, NLMIXED, GENMOD, CATMOD, LOGISTIC, TTEST, FREQ, MEANS, UNIVARIATE, TABULATE, SQL, PRINT, REPORT, and GPLOT.
• Operating systems: Unix, windows (NT, 2000, XP), Solaris 8,
• Corresponding software package: SAS, R/S-Plus, STATA, SUDANN and STATA, Microsoft Office(Access, Excel, PowerPoint and Word)
• Computer Language: C++

Center for Health Promotion and Disease Prevention, UNC-Chapel Hill
8/2006 – 6/2007
Junior Biostatistician

Department of Biostatistics, UNC School of Public Health
5/2006 - 8/2006
Summer Part-time Job

School of Dentistry, University of North Carolina at Chapel Hill
8/2004 - 5/2006
Junior Biostatistician

Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill
8/2002 - 8/2004
Statistical Analyst

University of North Carolina, Chapel Hill, NC
8/2006 - 12/2006
Teaching Assistant, Department of Biostatistics

Statistics, Korea University, Seoul, Korea
1996 & 1998
Instructor
Statistical Data Analysis using SAS & Excel and Exercise of the Statistical Theory

PAPER IN PREPARATION

Moonsu Kang, and Pranab K. Sen,
False discovery rate in microarray studies

SELECTED ABSTRACTS / PRESENTATIONS

False discovery rate in microarray studies May 2007
Contributed Speaker, Frontiers in applied and computational mathematics, Newark, USA

False discovery rate in microarray studies August 2007
Contributed Speaker, JSM (Joint Statistical Meetings) Salt Lake City, Utah

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