Principal Statistical Methodologist (UK)
HybridBerkshire, Maryland, United States
Job Summary
Develop and apply advanced computational and statistical methods, including machine learning and AI, to inform drug development design, analysis, and decision-making. Build robust, reusable tools and workflows that integrate these approaches into routine use while maintaining software quality and reproducibility. Partner with cross-functional colleagues to identify high-leverage opportunities for data-driven methods and translate complex technical concepts into clear insights for both technical and non-technical audiences. Contribute to internal capability building by sharing tools, code, and methods across the organization, while enhancing the group's external profile through scientific publications and conference presentations. This role operates within the Statistical Innovation team in Biometric and Data Sciences, utilizing modern simulation approaches and causal inference to support real R&D decisions.
Required 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
- 3+ years of experience within the pharmaceutical industry
- 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
- 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
- Internal applicants should be in their current job for at least 12 months
- Internal applicants must meet performance standards
- Internal applicants are not on formal corrective/disciplinary process (PIP), warning, final warning, or compliance warning letters within the last 12 months
Desired Qualifications
- Experience in advanced computational methodology for clinical development (early to late stage)
- Direct entry may be considered
- Experience with large language models, causal inference, synthetic data, or digital-twin/simulation approaches
- Experience with R
- Experience with Python
- Experience with software-engineering practices such as version control, testing, and reproducible workflows
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