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Real World Data Analyst

2 months ago

Uxbridge, United Kingdom Gilead Sciences Europe Ltd. Full time
About the Role

Gilead Sciences Europe Ltd. is seeking a highly skilled Data Analyst to join our Real World Evidence (RWE) Analytics team. As a Data Analyst, you will play a critical role in designing and conducting statistical analyses of real-world data to assess the value of our therapies.

Key Responsibilities
  • Develop and quality control (QC) technical functional listings (TFLs) for protocols/reports/manuscripts from RWE research conducted to assess the value of Gilead therapies using real-world data (e.g. claims and electronic health records).
  • QC programming for descriptive and complex studies using real-world data.
  • Conduct analyses and develop specifications for descriptive and complex statistics in studies using real-world data.
  • Write the statistical analysis plan (SAP) for descriptive and complex studies using real-world data, including from internal Gilead-sponsored prospective cohort studies, claims, charge master and electronic health records in collaboration with the RWE Therapeutic Area 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 real-world data (e.g. claims and electronic health records).
  • Work with RWE researchers to generate code lists for new measures in real-world data.
Requirements
  • 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 real-world data.
  • 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.