Real World Data Programmer (Assoc Director)Ireland - Dublin Pharmacovigilance & Epidemiology Regular
Job DescriptionAt Gilead, we're creating a healthier world for all people. For more than 35 years, we've tackled diseases such as HIV, viral hepatitis, COVID-19 and cancer – working relentlessly to develop therapies that help improve lives and to ensure access to these therapies across the globe. We continue to fight against the world's biggest health challenges, and our mission requires collaboration, determination and a relentless drive to make a difference.
Every member of Gilead's team plays a critical role in the discovery and development of life-changing scientific innovations. Our employees are our greatest asset as we work to achieve our bold ambitions, and we're looking for the next wave of passionate and ambitious people ready to make a direct impact.
We believe every employee deserves a great leader. People Leaders are the cornerstone to the employee experience at Gilead and Kite. As a people leader now or in the future, you are the key driver in evolving our culture and creating an environment where every employee feels included, developed and empowered to fulfill their aspirations. Join Gilead and help create possible, together.
Job Description Real-World Evidence (RWE) has become a vital complement to the traditional clinical trial in the demonstration of the value and safety of new medicines. Recognizing its importance, Gilead has established a core RWE Analytics group within the Clinical Data Sciences (CDS) - RWE Organization to support the use of RWE across the discovery, development, and lifecycle of our medicines. Members of this group will be fully embedded alongside their Clinical, Real-World Evidence (RWE), Medical Affairs Research (MAR) and Global Value & Access (GV&A) colleagues, helping to develop and execute their RWE strategies.
As a member of the core RWE Analytics group, individuals will have access to real-world databases in-licensed across Gilead and Kite and act as the stewards of Gilead's best practices, standards, and methodologies underlying the use of real-world data (RWD).
Key Responsibilities Develop and QC TFLs for protocols/reports/manuscripts from RWE research conducted to assess the value of Gilead 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 Gilead-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 an extensive background of relevant, post-graduation experience.Doctoral level training with 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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