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Statistical Research Associate Resume


  • A challenging position as a SAS programmer in which I can fully utilize and further develop the professional skills that I have acquired in my ten years of applied research.
  • Have an established familiarity of SAS programming with ten years of experience in modeling, prediction, analysis, design, development and test
  • Intensively worked on SAS/BASE, SAS/STAT, SAS/MACRO, SAS/GRAPH, SAS/IML
  • Have eight years statistical application in biological field
  • Expertise in analyzing and coordinating biological data, generating reports, tables, listings and graphs
  • Deeply understand statistical techniques and methodology, such as Normality test, ANOVA, t - tests, multiple comparison tests (Planned and Post HOC), Chi square tests, sampling, probability, regression
  • Possess strong ability to quickly adapt to new applications and methodology
  • Be able speak and write in English and Mandarin


  • Positive and motivated
  • Fast Learner
  • Hard worker
  • Be able work in irregular hour
  • Be able work individually or in a team
  • Deadline supported work capacity
  • Projected goal orientation
  • Analytical in nature
  • Accuracy


Programming Language: SAS, Java, Adobe Dreamweaver, Internet Programming, SitePublisher, PHP, MySQL

Office Package: MS Word, MS Excel, MS PowerPoint, MS Outlook Express, Photo Editor, Endnote, Acrobat Adobe Pro, Google Earth

Operating Systems: Windows 2000, Windows XP, Windows Vista, Windows 7



Statistical Research Associate


  • Develop statistical models
  • Develop experimental design and conduct studies
  • Collect, analyze and interpret experimental data
  • Collect literature, data - oriental information and build database
  • Plan, organize and coordinate lab and field studies
  • Write six papers and many analysis reports


Statistics Research Assistant


  • Develop a complicate nonlinear model to predict body temperature in cattle challenged by hot cyclic chamber temperatures
  • Develop model for predicting seed damage in plants
  • Conducted longitudinal data analyses, multiple regression, multivariate analysis

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