Statistical Programmer II

Statistical Programmer II

Everest's global biometrics team is embarking on an exciting phase of growth and we are looking to expand operations to the Philippines. If you're driven by data, eager to make a meaningful impact, and want to be part of a Statistical and Data Management Center of Excellence, we invite you to join our global biometrics team and help shape the future of clinical research.

Everest is known in the industry for its high quality deliverables, superior customer service, and flexibility in meeting clients' needs. A dynamic organization with an entrepreneurial origin, Everest continues to experience exceptional growth and great success.

Quality is our backbone, customer-focus is our tradition, flexibility is our strength…that's us…that's Everest.

To drive continued success in this exciting clinical research field, we are seeking committed, skilled, and customer-focused individuals to join our winning team in Statistical Programmer II positions remotely from a home-based office near Manila, Philippines in accordance with our Work from Home policy.

Key Responsibilities:

• Develop and review SDTM and ADaM dataset specifications, annotated Case Report Forms (CRFs), and other related programming documentation (e.g., define.xml files, reviewers' guides for SDTM and ADaM datasets) based on Everest company Standard Operating Procedures (SOPs) and Working Instructions (WIs), which are compliant to the pharmaceutical clinical research industry standards, conventions, regulatory requirements, and Sponsor-specific requirements.
• Program and validate SDTM and ADaM datasets following approved dataset specifications, these may include datasets for Clinical Study Reports, clinical trial efficacy and safety data integrations (Integrated Summary of Safety and Integrated Summary of Efficacy), and other data analysis and reporting needs such as ad hoc and exploratory data analyses.
• Develop SAS programs to generate and validate statistical output reports of trial data based on the Statistical Analysis Plan and statistical TLGs mock-up shells.

Clinical Data Acquisition and Cleaning Support:

• Develop and test SAS programs for clinical trial database logical checks and reports for ongoing data review.

Document data and programming information in accordance with Everest company SOPs and WIs; achieve audit readiness during and at the end of the clinical trials for internal and external quality and compliance audits.

Achieve a high rating for each of the defined Everest company and trial Sponsor-specific statistical programming key performance indicators.

May lead programming efforts for assigned projects.

May participate in the development and maintenance of clinical trial data and statistical reporting standards to ensure that the company standards are up-to-date and in-line with pharmaceutical industry standards, conventions, and regulatory requirements.

Provide technical guidance and support to less experienced Statistical Programmers in the study teams.

Maintain current knowledge of pharmaceutical clinical research industry standards, conventions, and regulatory requirements, and the knowledge of clinical trial Sponsor-specific requirements when applicable.

Comply to Everest company-specific and clinical trial Sponsor-specific training requirements.

Develop and provide expertise in other programming and system administration areas when required.

Qualifications:

A Master's or Ph.D. Degree in statistics, biostatistics, epidemiology, public health, bioinformatics, mathematics, and computer sciences with at least two years' experience in clinical trial statistical programming and/or data analysis.

We thank all interested applicants, however, only those selected for an interview will be contacted.

Everest is committed to upholding the principles of dignity, independence, integration, and equal opportunity. We welcome and encourage applications from people with disabilities, and upon request we will provide accommodations for candidates participating in any part of our recruitment and selection process.