Clinical Data Analytics Lead

Key Responsibilities:

• Transform complex clinical data into intelligent, actionable insights that inform decision-making across Clinical Trial Teams
• Drive collaboration across Data Quality Teams to enhance data strategy, data quality, site experience, and patient safety utilizing the Disease Area, Data Standards & Protocol Knowledge.
• Partner with Central Monitoring (CM) and Data Management) Leads to promote risk-based data review approaches within CDO.
• Leverage data analytics and visualisation tools such as Power BI and Spotfire to uncover trends, patterns, and emerging risks
• Contribute to advanced data quality initiatives such as anomaly detection and continuous improvement of insight generation capabilities
• Drive risk‑based analytics by proactively identifying Critical-to-Quality (CtQ) risks and prioritizing data review through advanced analytical approaches.
• Implement operational interventions that enhance data quality and accelerate study cycle times.

Essential Requirements:

• Extensive experience in clinical data operations within clinical trials, with strong understanding of clinical trial delivery. Ideally a minimum of 10-12 years working experience in a Life Sciences environment with extensive exposure to Clinical Trials and Clinical Operations
• Proven track record in driving operational excellence within pharmaceutical or clinical research environments
• Proven industry-based experience in clinical data management, clinical programming, RbQM, analytics, and technology transformation.
• Strong ability to generate intelligent, data-driven insights and influence decision-making across clinical teams
• Experience using data visualisation tools such as Power BI, Spotfire, or similar platforms to uncover trends and insights
• Demonstrated capability in data storytelling, translating complex data into clear, impactful narratives for stakeholders
• Knowledge of technology transformation, including automation, cloud platforms, and business intelligence tools, with awareness of programming languages such as SAS, R, or Python
• Strong understanding of clinical data technologies (e.g. EDC, clinical data hub, statistical computing environment, metadata management tools, J-Review), data standards such as Clinical Data Interchange Standards (Consortium Study Data Tabulation Model and Analysis Data Model), and regulatory compliance and data governance requirements
• Fluent written and verbal English essential