Clinical Data Analytics Lead

Job Description Summary

#LI-Hybrid
This role can also be offered on a remote working basis for UK, subject to eligibility criteria.
Location: Westworks, London, United Kingdom
Other Locations: Dublin, Ireland
This role is based in London, United Kingdom. Novartis is unable to offer relocation support: please only apply if accessible. Novartis cannot sponsor visas for these locations.
This role is also available in Dublin, Ireland. Please apply to the advertisement for this location. For London we offer a Hybrid working arrangement with an expectation of 12 days per month onsite. Remote working arrangement can be considered, subject to eligibility criteria. If your location is only suitable for remote work this can be discussed during the interview process.
Step into a role where your insight can directly improve the way clinical trials are understood, managed, and advanced. As Clinical Data Analytics Lead, you will turn complex clinical trial data into clear, meaningful stories that help teams act earlier, think smarter, and raise the quality of decisions across Clinical Data Operations. Working at the intersection of analytics, data quality, and trial execution, you will help uncover patterns, trends, and opportunities that strengthen study oversight, support risk-based approaches, and bring fresh, actionable insight to the teams shaping patient outcomes.

 

Job Description

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

 

Skills Desired

Clinical Operations, Clinical Trial Process, Clinical Trials, Clinical Trials Analysis, Clinical Trials Operations, powerbi, Python (Programming Language), R Analytics, risk based quality management, R Programming, R Statistics, Statistical Analysis Systems (SAS) Programming, Technology Change, Technology Transformation