Veristat is expanding our global footprint, from our home office in Boston, up to Canada, down to RTP, and over to Taipei.
We do things differently than large CROs. Do you value scientific integrity and a collaborative team environment? If so, you will feel right at home here with dedicated opportunities to discuss your achievements and grow your career through quarterly feedback conversations.
We invite you to learn more about us at our website www.veristat.com.
Now hiring for our February 2020 program class!
Through comprehensive training and development, the Statistical Programming Apprentice (“SPA”) will train with Statistical Programmers and Statisticians to learn the overarching principals and processes of clinical development, including but not limited to Good Clinical Practice and ICH Guidelines.
Systematically and through hands-on learning, the SPA will learn to...
Program clinical data (using SAS) according to CDISC guidelines including: producing SDTM Data Sets and Analysis Data Models (ADaM), and performing quality control analyses.
Create tables, listings and figures (TLFs) for case study reports, summaries of safety and efficacy, ad hoc analyses, and electronic submission deliverables, such as datasets, data documentation, programs, programming table of contents, and patient profiles.
Experience + Requirements
Recent graduate of a Bachelor’s or Master’s program in a related field, such as life sciences, computer science, applied mathematics, etc.
Strong interest in pursuing a career in clinical research, particularly in the areas of statistical programming and biostatistics
Familiarity with basic pharmacological and/or statistical principles and aptitude for new technologies preferred
Strong English communication skills, written and oral, required; prior experience with technical or scientific writing a plus
Strong collaboration skills and experience working in teams
Proficient in Microsoft office, including Excel, Outlook, Word, and PowerPoint
High level of detail orientation, and the ability to work on multiple tasks under tight timelines