Principal Statistical Methodologist

About the role

You will help shape how evidence is generated, modeled, and communicated across the drug development lifecycle, bringing modern computational and machine-learning methods to bear on real R&D decisions. You will develop and apply advanced data-driven and model-based approaches—drawing on statistics, machine learning, and AI—translate them into robust, reusable tools, and partner across functions to put them to work where they impact drug development decision making. You will also contribute to the group's scientific profile through publications and external collaboration.

Who you will work with

You will be working in a team that provides statistical consultancy across therapy areas and development stages, partnering closely with colleagues in clinical development, regulatory strategy, data science, and medical affairs. The team values curiosity and problem solving, practical innovation, clear communication, and collaboration, bringing novel quantitative and computational approaches into real study decisions and sharing learnings with the wider scientific community.

What you will do

• Develop and apply advanced computational and statistical methods, including machine learning, AI, and scenario evaluation using modern simulation approaches, to inform design, analysis, and decision-making across development.
• Build robust, well-engineered, reusable tools and workflows that bring these methods into routine use, with attention to reproducibility and software quality.
• Bring a quantitative lens with appropriate rigor to emerging problems such as synthetic and external control data, causal inference, and digital-twin or simulation-based approaches.
• Partner with statisticians and cross-functional colleagues to identify where computational and data-driven methods add the most leverage, and translate complex approaches into clear insight for technical and non-technical audiences.
• Contribute to internal capability building by sharing tools, code, and methods across the team and wider organization.
• Contribute to the group's external profile through scientific publications, conference presentations, and participation in cross-industry initiatives and working groups.

Minimum Qualifications

• Doctoral degree in statistics, biostatistics, mathematics, computer science with a strong quantitative/statistical component, or a closely related discipline with a solid grounding in statistical inference and uncertainty.
• Minimum of 3 years of experience within the pharmaceutical industry.
• Experience in advanced computational methodology for clinical development (early to late stage) is an advantage. Direct entry may be considered.

Preferred Qualifications

• Strong, multi-language scientific programming skills (R and Python preferred; software-engineering practices such as version control, testing, and reproducible workflows a clear advantage).
• Demonstrated expertise in machine learning and/or AI methods, with hands-on experience applying them to real problems; experience with large language models, causal inference, synthetic data, or digital-twin/simulation approaches is a strong advantage.
• Sound knowledge of ICH guidelines and understanding of regulatory requirements from major health authorities.
• Ability to work effectively with autonomy, manage multiple priorities, and deliver timely, high-quality outputs.
• Clear written and spoken communication in English, including the ability to explain technical concepts to non-technical audiences.