Biostatistician with Security Clearance

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Job description

Overview 6 month contract DNI is supporting the CDC's IISSB with the goal of advancing IIS's through statistical analysis, coverage assessment and dissemination of data from Immunization Information System. Responsibilities Perform advanced statistical analysis and mathematical statistical work of Immunization Information Systems (IIS) data using SAS Research literature for covariates associated with vaccination and predictive modeling Use statistical methodologies to extrapolate IIS vaccination coverage data and assess the quality of IIS data in comparison to other national sources of immunization data Develop composite variables to summarize a group of related covariates for modeling purposes Document methodologies and results, and summarize findings; Perform data analysis, ensuring statistical accuracy in planning data collection procedures Conduct analytical investigations using scientific methodology and problem solving Prepare routine reports on studies and projects or apply knowledge of the technical rules and procedures to scientific projects and programs to maintain documentation and prepare analyses and reports of findings for tests and studies Develop Project summary including detailed methodologies, results, and discussions Assist with the development of study protocols (e.g., study design, power and sample size calculations, statistical analysis sections, etc.), manuscripts and other scientific documents. Assist customer staff with the planning, organization, and analysis of scientific studies, generate IIS-based vaccination coverage assessments reports using IIS awardee data Provide ad-hoc statistical consultation including method design, analysis, and reporting of results Prepare technical reports, abstracts, scientific presentations, and/or peer-reviewed publications for dissemination to customer staff and to the broader scientific community Conduct trainings on statistical topics and statistical software Qualifications To be considered for this position, you must minimally meet the knowledge, skills, and abilities listed below: Master's Degree in Statistics, Biostatistics, Engineering, Computer Science, Mathematics, or similar field, and 10 years of related work experience and graduate level coursework in statistics 5 years of experience with quantitative analysis and data interpretation 5 years of experience with advanced statistical analysis and programming in SAS Excellent organizational skills, commitment to generating accurate data, ability to meet short deadlines, and demonstrated experience in multi-tasking Experience with cleaning data for analysis (i.e., data munging) Strong interpersonal and teamwork skills Strong oral and written communication skills; Experience with both written and oral presentation of data and the ability to interact with senior research staff and external stakeholders Strong oral and written communication skills, particularly with multidisciplinary staff from different organizations/agencies Adheres to reproducible research and applies well-precedented techniques Candidates must be U.S. citizens. Preferred Qualifications: Candidates with these desired skills will be given preferential consideration: D. in Statistics, Biostatistics, Engineering, Computer Science, Mathematics, or similar field, and 7 years of related work experience and graduate level coursework in statistics Experience working with Immunization data and/or Immunization Information Systems (IIS) Experience developing advanced data visualization applications (RShiny, D3, etc.) Experience utilizing Python for developing machine learning models and algorithms, including the use of the following Python machine learning libraries: Numpy, Scipy, Scikit-learn, Theano, TensorFlow, Keras, PyTorch, Pandas, and Matplotlib. Experience mathematical modeling (e.g., SI, SIR, SEIR, etc.) and/or using AI or machine learning packages (e.g., caret, randomForest, nnet, e1071, glmnet, tree, etc.) Experience with concepts such as complex sample surveys, survival analysis, and mixed effects modeling

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