Sr Data Analyst
Senior Data Analyst sought by University of Arkansas for Medical Sciences in Little Rock, AR. Master’s or equiv in Statistics, Mathematics, Computer Science or related field, plus 3 yrs exp. Must pass standard Data Analytics proficiency assessment. Review complete job description and apply online at https://jobs.uams.edu job # 81071.
Duties: Develop complex and efficient data analytic and statistical programs to manipulate healthcare data to create research-ready datasets for empirical analyses. Import and longitudinally cross-link data from a variety of sources to develop research-ready analytic datasets. Import and integrate large, complex datasets using SAS, including administrative claims data and enrollment files from a variety of insurance payers. Execute basic descriptive and inferential statistical analyses. Communicate and collaborate with the Director of Research and Evaluation and the Assistant Director of Analytics on translating policy questions into initial analytic plans based on policy questions. Communicate and collaborate with project teams made up of other staff who possess data, policy, and other skillsets. Create written data analytic plans with detailed data management procedures to transform raw healthcare data into research-ready analytic datasets. Write methodological components of technical reports following accepted protocols. Maintain detailed programming documentation for all projects, including annotated program code, dataset codebooks, and statistical output. Identify relevant background information to inform analytic plans designed to answer policy-related questions. Identify new techniques and approaches to enhance current or future analytic reporting.
Qualifications
Requirements: Master’s degree or equivalent in Statistics, Mathematics, Computer Science, or a related field plus three (3) years of statistics, data analysis, programming, or related experience managing data with large, administrative datasets; manipulating large datasets using advanced management and statistical software such as SAS or SQL; using advanced SAS functions to create time-dependent analytical variables; utilizing statistical techniques, including univariate statistics, comparisons, and regression modeling; applying, calculating, and interpreting descriptive and inferential data; using the following tools and technologies: Microsoft Office, SQL programming and ArcGIS software. Must pass standard Data Analytics proficiency assessmen
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