Research Statistiscian in the The Biostatistics practice area of RTI Health Solutions – RTI Spain
As a member of the Barcelona team, you will work effectively as a member of a cross-functional team, performing
analyses of pharmaceutical research data, particularly cross-sectional survey and longitudinal studies in large
claims or electronic medical record databases in North America and Europe. You will develop statistical analysis
plans and perform programming of analyses of moderate to high technical complexity while requiring limited
At RTI-HS, you will be provided the opportunity to conduct meaningful work in a collaborative, cross functional
environment. We are focused on your career development and provide a generous benefits package.
As a part of the application process, please include a cover letter, in English, highlighting your experience
performing analyses of pharmaceutical and/or observational research data.
Additional responsibilities include, but not limited to:
- Select among existing approaches and techniques to complete varying assignments and learn new
methods in response to project demands
- Ensure quality of statistical analysis and programming for supported projects
- Write statistical sections of study reports under supervision
- Prepare presentations for internal audiences
- May provide statistical consulting to internal colleagues
- M.S. in Statistics/Biostatistics or related field, with coursework in Epidemiology preferred
- 4 years of experience leading statistical tasks in pharmaceutical/healthcare research and related environments.
- Demonstrated experience in analysis planning, programming and interpretation of results.
- Demonstrated SAS programming skills.
- Demonstrated communication skills, both written and oral, including proficiency in English
- Demonstrated organization skills
- Epidemiological background or experience, especially with observational studies and analyses of large, complex databases such as electronic medical records and claims databases.
- Experience using and transforming observational data into a common data model (CMD), including transforming variables and harmonizing variable names as defined in the CMD.
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