RWE Data Analyst

· Develop and QC TFLs for protocols/reports/manuscripts from RWE research conducted to assess the value of the client's therapies using RWD (e.g. claims and EHR). · QC programming for descriptive and complex studies using RWD. · Conduct analyses and develop specifications for descriptive and complex statistics in studies using RWD. · Write the statistical analysis plan (SAP) for descriptive and complex studies using RWD, including from internal client-sponsored prospective cohort studies, claims, charge master and EHR in collaboration with RWE TA lead. · Understand methods and programming to support Comparative Effectiveness Research (CER) analyses, as well as analyses of patient-reported outcomes (PRO) or other patient outcome data. · Work with RWE researchers to generate code lists for new measures in RWD. Knowledge, Skills and Experience · Master’s degree (e.g. MA, MSc, MPH) in Biostatistics, Epidemiology or related discipline, such as Outcomes Research from an accredited institution, with a minimum of eight (8) years of relevant, post-graduation experience. · Doctoral level training with a minimum of two (2) years of relevant experience is preferred. Direct experience in lieu of academic training is acceptable. · Knowledge of real-world data and experience in observational research study design, execution and communication. · Strong track record of analysis of a broad range of RWD. · Formal training in Programming and demonstrated proficiency in statistical analysis programs commonly used in life sciences (e.g. SAS, R). · Understanding of epidemiology or outcomes research and the application of retrospective or prospective studies to generate value evidence. · Ability to effectively communicate statistical methodology and analysis results. · Ability to work effectively in a constantly changing, diverse, and matrix environment. · Knowledge of US secondary data sources required; additional experience with international data sources is preferred. · Knowledge and experience in qualitative analysis and data sets (e.g., free-text natural language processing, survey data) is preferred.

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