Statistician Data Analyst And Programmer

Title : Statistician Data Analyst And Programmer

Location : Rockville MD 20818

Overall Position Summary and Objectives

Under this task order the contractor will independently provide epidemiologic and statistical services to satisfy the overall operational objectives of the NIMHD Division of Intramural Research. The primary objective is to provide services and deliverables through the performance of support services. We are seeking one fulltime statistician/data analyst/programmer to support the research activities of the NIMHD Division of Intramural Research.

Min Education Masters

Certifications & Licenses

• Doctorate degree in Biostatistics epidemiology statistics or a closely related field
• Applicants with publications in peer reviewed Journals are preferable
• Preferred candidates with Health Disparities research experience

Skills

1. Scientific Data analysis
2. Statistical modelling
3. Algorithm development
4. Data visualization
5. Machine learning
6. Proficiency in using advanced statistical methods
7. Linear and nonlinear regression
8. Expertise to perform the duties of the position which include working with NIMHD DIR investigators and fellows to perform data management and data analysis for both primary data collection studies and secondary/publicly available datasets in a timely manner
9. Experience conducting statistical analyses in complex survey data or other secondary data sources that involve sampling weights (e.g. NHANES National Health Interview Survey NHIS Medical Expenditure Panel Survey MEPS)
10. Experience with structural equation modelling including but not limited mediation analysis effect measure modification moderated mediation analysis latent class analysis (LCA) principal components analysis (PCA) and other dimensionality reduction methods (structural equation modelling); non parametric statistical methods; quasiexperimental statistical analyses (e.g. differenceindifference)
11. Analyze studies using highdimensional longitudinal clustered multilevel and repeated measures data
12. Familiarity with Bayesian statistics and simulation modelling
13. Experience in working with students or trainees in teaching data analytic skills
14. Clean condense merge and reformat data into files that are appropriate for data analysis and data sharing including preparing deidentified datasets and documentation for external users
15. Create variables as needed for analyses and document methods and definitions for all variables created (e.g. data dictionary)
16. Expertise in performing statistical analyses using multiple statistical analysis software packages
17. Excellent analytical organizational and timemanagement skills A drive to learn and master new technologies statistical methods and techniques
18. Experience conducting statistical analyses in electronic health records (EHR/EMR) and administrative claims
19. Data cleaning
20. Data analysis
21. Epidemiology
22. Data mining

Software

• MPlus
• SUDAAN
• ArcGIS
• R
• SPSS
• Python
• SAS
• STATA
• C

Field of Study

• Statistics and Decision Science
• Computer Programming and Data Processing
• Applied Mathematics
• Management Information Systems and Statistics
• Mathematics and Computer Science

Statement of Work Details

Performs experimental investigations and similar research projects utilizing extensive applications of mathematical and statistical methodologies.

• Perform statistical analysis using novel methods and algorithms.
• Assist researchers with the planning implementing and analysis of research projects.
• Perform data analysis including model building analysis assessing trends determining correlations testing for heterogeneity and compiling and communicating results to investigators to participate in the interpretation of results and planning of further analyses.
• Provide statistical advice and consultation to the investigators in study design data management choice and application of statistical methods data analysis and interpretation of statistical results.
• Carry out statistical analyses on issues via descriptive analyses causal inference predictive modelling and other univariate and bivariate and multivariate analytic methods.
• Perform advanced epidemiologic and statistical analyses suitable for studies of health disparities and minority health including (but not limited to): linear and nonlinear regression modelling; survival analysis; time series analysis; propensity score matching weighting standardization multiple imputation missing data weighting censor weighting and small area estimation to account for confounding missing data loss to follow up selection bias and other forms of potential bias in studies
• Conduct statistical analyses; perform data cleaning and formatting data harmonization and data analyses; and prepare results for publication from intervention studies observational studies and secondary data analysis projects using complex survey data hospital/medical records administrative data or other data sources
• Advanced epidemiologic and statistical methods suitable for studies of health disparities and minority health including (but not limited to): linear and nonlinear regression modelling; survival analysis; time series analysis; propensity score matching weighting standardization multiple imputation missing data weighting censor weighting and small area estimation to account for confounding missing data loss to follow up selection bias and other forms of potential bias in studies
• Design and conduct statistical analyses using complex survey data or other secondary data sources that involve sampling weights (e.g. NHANES BRFSS National Health Interview Survey NHIS Medical Expenditure Panel Survey MEPS Current Population Survey and various supplements)
• Meet with data customers inside and outside the DIR to assess dataset requirements.
• Perform statistical analyses of large complex datasets preferably using SAS for population health research using existing NIH and publicly available datasets or data collected by NIMHD investigators.

Develops original computer code and programs for the application of new mathematical and statistical theories for the solution of proposed problems related to various scientific studies.

• Perform data programming analysis and presentation by preparing charts tables and graphs using software such as R SAS and STATA.
• Ensure that all data products (dynamic reports tables and graphics) are reproducible from the original source data by maintaining clear commented and consistent code and organization of files and folders.
• Create interim dynamic reports that weave together text code output tables and graphics and document all procedures and code used for data cleaning and analysis.
• Develop and systematically apply data classification schemes and process and combine data sets for analysis from diverse sources.
• Design and conduct statistical analyses using hospital/medical records administrative data and other primary and secondary data sources
• Design and analyze studies using highdimensional longitudinal clustered multilevel and repeated measures data
• Design and conduct statistical analyses using complex survey data or other secondary data sources that involve sampling weights (e.g. NHANES BRFSS National Health Interview Survey NHIS Medical Expenditure Panel Survey MEPS Current Population Survey and various supplements).
• Develop and implement methods and procedures for the collection processing compilation cleaning and analysis of data in collaboration with DIR investigators and trainees.

Utilizes statistical software packages to manage maintain and analyze large complex statistical databases.

• Research methods in data analysis revise study forms graphically display analytic results collaborate in writing or editing drafts of manuscripts for publication.
• Provide a crosstabulation descriptive analysis using standard statistical procedures rate standardization stratification of data and model building.
• Recommend appropriate statistical techniques for analysis of research data and prepare statistical reports analyze data and use statistical software packages and programs such as SAS and R.
• Implement and validate cuttingedge algorithms and new statistical methodologies to analyze diverse sources of data to answer research questions.
• Conduct statistical analyses; perform data cleaning and formatting data harmonization and data analyses; and prepare results for publication from intervention studies observational studies and secondary data analysis projects using complex survey data hospital/medical records administrative data or other data sources.
• Generate tables and graphics for abstracts manuscripts and presentations
• Prepare for publication results from clinical trials intervention studies observational studies and secondary data analysis projects using complex survey data hospital/medical records administrative data or other data sources.
• Interpret and communicate results of analyses in written and oral formats.

Enters and verifies data fields and data dictionaries.

• Ensure that appropriate variables are captured in the constructed databases.
• Format databases to allow merging of spreadsheets for statistical analyses and to optimize planned analyses
• Record Data into a format appropriate for processing.
• Apply statistical techniques to produce meaningful tables and graphs using appropriate software
• Provide support with data sharing including public repositories.
• Work with staff to prepare and standardize data for the database.
• Perform routine and general data management.
• Prepare tables and figures from data analyses.
• Perform database searches and assemble datasets.
• Analyze studies using highdimensional longitudinal clustered multilevel and repeated measures data.
• Clean condense merge and reformat data into files that are appropriate for data analysis and data sharing including preparing deidentified datasets and documentation for external users
• Create variables as needed for analyses and document methods and definitions for all variables created (e.g. data dictionary)
• Transfer data between software dataset creation (merge and concatenation) data cleaning (identify and correct data entry errors and missing values) and data transformation (create and categorize variables and impute data).
• Check and confirm the accuracy of calculations conducted by collaborating programmers analysts and presenters to guard against mistakes in design conduct or presentation of risk estimates.
• Collect and refine new data and refine existing data sources.
• Create data entry applications to improve data collection and management.
• Enhance data collection strategy and procedures for primary and secondary data sources including recovered data sources such as scans and microfilms of paper archives.
• Conduct data collection/entry management cleaning and manipulation activities.
• Creates Data Workflow Processes.

Collects and analyzes mathematical data and performs descriptive and missing data analyses.

• Perform data analysis of data sets involving statistical procedures varying in complexity from simple bivariate tests to advanced regression methods for longitudinal data analysis and timetoevent analysis; determine correlations between variables.
• Perform data analysis including crosstabulation descriptive analysis using standard statistical procedures as well as model building (logistic regression conditional logistic regression).
• Assist staff in conducting evaluations and analyses of programs using appropriate methods and tools and perform data management and carry out statistical analysis for assigned research projects.
• Process and analyze data using blindsource separation techniques.
• Organize manage and design data files and plans for associated statistical analysis.

Tracks and documents all modifications errors and changes to all databases and decisions.

• Prepare and/or update data tables and figures methods sections of manuscripts reports and other documents for presentation and/or publication.
• Take lead of the storage tracking internal review and retrieval of information documentation and datasets for all assigned projects and projects of any subordinates.
• Perform data cleaning formatting variable recoding data harmonization and data quality checks and data management and manipulation
• Transfer data between software and create datasets (merge and/or concatenation) data cleaning (identify and correct data entry errors and missing values) and data transformation (create and categorize variables and impute data).
• Review literature and create bibliographies research methods in data analysis revise study forms graphically display analytic results and collaborate with staff on writing and editing drafts of manuscripts for publication

Develops and coordinates the training program for staff in statistical and mathematical analysis.

• Attend all lab meetings lab checkins and other researchrelated meetings as requested by investigators or trainees.
• Report either verbally and/or in writing regular updates on the progress of their work to investigators.
• Provide expertise on epidemiologic and statistical research methods as needed for research projects protocols and proposals
• Train trainees on developing statistical analytic codes to analyze quantitative data to achieve research objectives and interpreting results from different statistical analyses
• Provide periodic training on contemporary epidemiologic and biostatistics analytics approaches to the NIMHD DIR.