Statistical Research Associate Resume
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SUMMARY:
- 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
PERSONAL STRENGTHS:
- 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
COMPUTER SKILLS:
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
WORK EXPERIENCE:
Confidential
Statistical Research Associate
Responsibilities:
- 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
Confidential
Statistics Research Assistant
Responsibilities:
- 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