Statistical Programmer II - Sponsor Dedicated

As a Statistical Programmer II in our sponsor-dedicated Flexible Solutions business unit, you are central to the successful delivery of complex clinical trials (phases I-III) for renowned, innovative and global top pharmaceutical companies. Oftentimes you will have the opportunity to lead studies through various phases of the clinical development plan and support submissions, regulatory questions, publications as well as ad hoc and post hoc requests.

You can be 100% home-based or if you prefer, you can work from our local office in your home country.

Your Responsibilities

• With sufficient experience assume the role of a Lead Statistical Programmer for CP studies only with support from senior programming staff.
• Plan, execute and oversee all programming activities on a study, including but not limited to, resource estimation, working within budget, meeting timelines, maximizing quality, interaction with other departments and the client, etc.
• Develop and maintain SAS programs to create SDTM and ADaM datasets and TFLs, and perform QC of SDTM, ADaMs and TFLs.
• Produce Define XML/PDFs, Analysis Results Metadata (ARM), annotated eCRFs and Reviews Guides to support SDTMs and ADaMs.
• With support from senior programming staff, develop specifications for SDTMs and ADaM datasets.
• Review SAPs and TFL shells from a programming perspective for studies with support from senior programming staff.
• Respond to QA and client audits with support from senior programming staff.
• And all other duties as needed or assigned.
  
  

Your Profile

• Minimum Bachelor’s degree, preferably in mathematics, statistics, computing, life science, health science, or related subjects.
• Typically, 1 - 2 years of SAS programming experience in the CRO or Pharmaceutical industry, or an equivalent combination of education and experience to successfully perform the key responsibilities of the job.
• Knowledge of CDISC requirements.
• Business fluency in English – both spoken and written – is a must.
  
  

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