Associate Director, RWE Analytics

Responsibilities

• Design real-world retrospective and prospective cohort studies supporting pipeline and post-marketing products.
• Define patient cohorts and health and economic outcomes.
• Program in data process, management, and statistical analysis on registry and large claims/EHR/EMR databases using SAS/SQL/R/Python, etc.
• Identify selection and application of optimal and appropriate statistical methods to health and economic outcome studies.
• Develop statistical analysis plan and statistical outputs to support internal business strategy and external communications.
• Assist in publications of health and outcome research studies including drafting and reviewing sections of methods and results.
• Assist in development of presentation material to internal and external stakeholders.
• Maintain awareness of new statistical methods, tools, and data sources to ensure that projects represent current state of science, and maintains professional knowledge by reading scientific journals, attending internal and external courses, and undertaking methodological research.

Qualifications

• A master’s degree or higher in quantitative sciences, e.g., epidemiology, biostatistics, psychometrics, computer science, or a closely related field with at least 10 years of hands-on analytical and research experience is required.
• Managing large administrative or medical records databases with SQL or Python programming languages is required.
• Statistical analysis with SQL, SAS, R or Python etc. languages is required.
• Knowledge of large administrative or medical records databases is required.
• Experience in statistical modeling of multivariable analysis, repeated measures, and specific data distribution to conduct clinical and outcome research is required.
• Experience with non-randomized designs is required.
• Experience with writing protocols in general and particularly the statistical methods sections of study proposals and/or proposal requests is required.
• Background in epidemiological material on etiology, incidence and prevalence of specific diseases, conditions, and therapies (e.g., treatment patterns, adherence, effectiveness, etc.) is preferred.
• Experience in advanced analytics (e.g., machine learning) and data interpretation is preferred.