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Statistical Programmer R

https://www.pop-science.co.uk/job-search/1823-statistical-programmer-r/clinical-research/central-london/job2026-05-08 08:06:552026-07-06 Pop Science
Job Type Temporary / Contract
Location Greater London
Area Central London, UK Central London UK Greater London
Sector Clinical Research
Salary £0 per year
Currency gbp
Start Date ASAP
Advertiser remoteapi
Job Ref 1505
Job Views 4
Description

Our client is seeking an experienced Statistical Programmer as a full time contractor for 6-months. The successful candidate will work closely with the Head of Statistics, an existing Statistical Programmer, and the wider research team to deliver on an exciting new project looking at the disease course of Atrial Fibrillation.

The primary focus of the role is to support the creation of CDISC-compliant datasets, primarily SDTM and, where needed, ADaM, for an observational real-world study. The successful candidate will be expected to develop programming solutions and dataset specifications in R, based on the protocol, eCRF, and study requirements. Depending on sponsor requirements, the role may also include support for submission-readiness activities and preparation of associated documentation.

Key responsibilities

· Lead the conversion of raw observational study data into CDISC SDTM datasets and drive the end-to-end development of ADaM datasets.

· Develop R code to transform source data into standardised structures.

· Review the study protocol, annotated CRF/eCRF, and related documentation to identify dataset and metadata requirements.

· Write trial design datasets/specifications, SDTM specifications, and ADaM specifications.

· Translate EDC/eCRF structures into detailed SDTM mapping specifications, including derivation and transformation logic.

· Prepare and maintain key submission-supporting documents, including:

o SDTM Review Guide

o ADaM Review Guide

o define.xml

o controlled terminology / codelist documentation

· Validate datasets and identify and resolve issues using Pinnacle 21 / OpenCDISC.

· Contribute to establishing a robust and reproducible programming workflow using GitHub and relevant R-based packages.

· Work collaboratively with statisticians, research strategists, and the existing statistical programmer to ensure datasets and documentation are accurate and fit for purpose.

· Support preparation of submission packages, if required.

· Perform independent double-programming and validation of datasets to ensure adherence to CDISC guidelines prior to sponsor delivery.

· Contribute to good programming practice, quality control, and documentation standards across the project.

Person specification

Essential experience and skills

· Proven experience converting observational / real-world study data into CDISC SDTM and, coupled with deep, hands-on experience generating ADaM datasets and specifications from scratch

· Strong hands-on programming experience in R, including building codebases and derivations.

· Strong experience with CDISC implementation and submission-supporting documentation.

· Experience using R-based CDISC/pharmaverse tools such as:

o sdtm.oak

o admiral

o other relevant pharmaverse packages

· Experience writing SDTM and ADaM specifications directly from protocol and CRF/eCRF documentation.

· Experience converting annotated CRFs / EDC structures into detailed mapping specifications and transformation logic.

· Familiarity with preparing define.xml, review guides, and controlled terminology documentation.

· Experience validating datasets using Pinnacle 21 or equivalent CDISC validation tools.

· Experience using GitHub for version control, code management, and reusable workflows.

· Strong attention to detail and commitment to quality and reproducibility.

Desirable

· Previous experience supporting regulatory submission packages.

· Experience in cardiovascular, thrombosis, or other observational registry studies.

· Familiarity with biomarker, laboratory, and patient-reported outcome data.

· Working knowledge of SAS, although this is not essential for this role.

#R #Statisticalprogrammer

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