Associate Director - RWE - Data Programmer

Job Description

Associate Director, Real World Data Programmer

Primary Location: Parsippany, NJ

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).

This position can sit at the following sites: Foster City (preferred), Seattle, Morris Plains, or Raleigh Business Center (NC)

Job Description

As a member of the CDS-RWE Analytics group, the RWD Analyst reports directly Head of RWE Analytics and is responsible for the design and conduct of statistical analyses of RWD to assess the value of Gilead therapies and perform data visualization and QCs TFLs to communicate results to internal stakeholders in Real World Evidence. The RWD Analyst will align with the Real-World Evidence Therapeutic Area (TA)-aligned Lead to conduct timely, relevant and rigorous analysis of RWD to address critical research questions, as well as collaborate with CDS to develop, refine, and scale data management and analytic procedures, systems, workflows, best practices, and other issues.

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
• 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)
• 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.