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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