Clinical Data Programmer

Role Summary

The Senior Clinical Data Programmer SAS is responsible for data analysis and business intelligence activities related to study related clinical and operational data for the purpose of facilitating clinical data inspection and quality control, as well as operational performance control of clinical sites by creating reports, visualisations and dashboards for Clinical Data Managers (CDM), Clinical Data Reviewers (CDR), Project Managers (PM) and other clinical study stakeholders. 

Qualifications

• A graduate qualification in computer science, data science, mathematics, life sciences or a related field. 

• A minimum of 5 years’ relevant experience in statistical programming, business intelligence, data analysis or data science within a pharmaceutical company, CRO or EDC software provider. 

• Practical experience using SAS and/or business intelligence tools (e.g. TIBCO Spotfire, Power BI), to interrogate clinical, laboratory or operational datasets is required. 

• Exposure to additional analytics, RBQM, centralised monitoring or workflow automation platforms is desirable.

Key Responsibilities

The Senior Clinical Data Programmer SAS partners with clinical study teams and other stakeholders to define, develop, validate, deploy and maintain fit-for-purpose programming and analytics solutions, including dashboards, graphs, tables, listings and reports.

1. Project/Clinical Studies

• Partner with CDMs, CDRs, PMs and other study stakeholders to define clinical, laboratory, external and operational data requirements and recommend fit-for-purpose dashboards, visualisations, listings and reports.

• Manage assigned programming deliverables from requirements gathering and specification through development, testing, deployment, maintenance and change control in accordance with applicable procedures.

• Extract, integrate, transform and analyse clinical study data from EDC, laboratory, operational and other sources using SAS and relevant technologies.

• Develop validated, reusable programs and analytical outputs that support data inspection, reconciliation, clinical and medical review, operational oversight and timely study decision-making.

• Develop and maintain analytics supporting risk-based quality management and centralised monitoring, including key risk indicators, quality tolerance limits, site performance oversight and identification of meaningful data trends and quality signals.