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Senior Real-World Data Analyst

East Brunswick, NJ

Posted: 12/01/2022 Job Number: 22499531

Job Description

SENIOR REAL-WORLD DATA ANALYST  
Position Duties:
  • Conducting health data analysis based on large databases such as administrative claims, electronic health records (EHR), and disease registry.
  • Independently conduct retrospective studies with limited supervision starting with applying inclusion-exclusion criteria to demographics, pharmacy, inpatient, outpatient claims data, building the cohort, and finally conducting statistical analyses.
  • Consultation with fellow researchers to help design and conduct retrospective observational studies.
  • Data analysis using SAS and SQL to support observational studies and clinical trial research.
  • Quality control of databases including electronic health records and claims databases to ensure accurate and up-to-date availability of data.
  • Management of large databases that include data cleaning, organizing, manipulation, and extraction.
  • Present findings and project updates to internal teams at periodic team meetings.
  • Will travel to various unanticipated client sites in the U.S.
  • Occasional remote work permitted.
 
 Requirements:
  • Master' s degree in Health Outcomes and Policy or similar.
  • 2 years of experience as a Senior Real-World Data Analyst, Graduate Research Assistant, or similar role working with health outcomes.
  • 2 years of SAS programming experience.
  • 2 years of SQL experience.
  • Knowledge of GLM or GENMOD procedure required.
  • Experienced in manipulating large patient claims databases to generate health outcome research report for publication.
  • Familiar with healthcare claims data, electronic health records data, patient longitudinal analysis experience, and diagnosis (ICD) procedure (CPT/ICD), and national drug code (NDC) coding systems.
  • Experience working with health outcomes, health services research, and/or Pharmacoeconomics and real-world observational study.
  • Ability to communicate statistical findings in scientific meetings.
  • Statistical background or knowledge of statistical methods such as multivariate analysis, logistic regression, generalized linear models.
 
 

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Vinatha Padiyar

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