BACKGROUND

RESPONSIBILITIES

• Provide statistical support in the design and analysis of surveys, randomized controlled trials, and risk factor assessments including modeling of data
• Develop statistical and mathematical models to describe findings and describe current trends and changes in trends
• Provide statistical input in the study design phase and analysis phase, as well as in careful review of the end product, focusing on appropriateness of statistical methods and validity of conclusions
• Establish and track time schedules for completing various phases of analytical work and development
• Design and conduct methodologically sound research and evaluation studies using univariate, bivariate, multivariate and other statistical testing approaches as appropriate
• Participate in the interpretation of study results and manuscript preparation
• May also provide programming support, which shall include designing and maintaining customized databases for data entry and tracking, querying custom datasets for analysis, supporting and troubleshooting users, developing new functionality as indicated, developing a detailed understanding of existing databases including but not limited to Project, and running descriptive statistics and other computations as requested
• Track, monitor, analyze, and report data from surveillance studies
• Merge, integrate and pool data across different sources
• Perform data management and analysis
• Compile and organize data and assure accuracy
• Contribute to the writing and editing of reports, manuscripts and other documents

REQUIREMENTS

• BS or equivalent and five years experience or a MS and three years experience
• Advanced R programming for developing statistical software and data analysis
• Proficiency with Excel

PREFERENCES

• Degree in Statistics, Computer Science, Public Health
• NIH or life science experience
• Minimum of three years experience in data management and statistical analysis
• Knowledge of study design and ability to identify and explore potential sources of bias and confounding and a thorough understanding of statistical methods including but not limited to GEE, linear and non-linear mixed effects models, ROC analysis, decision curve analysis, analysis of agreement, analysis of reproducibility and survival analysis. Bootstrap resampling procedures are often required to calculate standard errors and 95% confidence intervals.
• Proficiency with packages such as SAS and Access
• Knowledge and experience in assuring data quality and validation and possess capabilities in basic and complex data analysis
• Ability to draft plans to resolve inaccuracies or other problematic situations
• Ability to research, learn and provide innovative and original thinking with regards to study design and in the use of established and new statistical methods

LOCALE

HOW TO APPLY