Principal Statistical Methodologist (Germany)
HybridMettmann, North Rhine-Westphalia, Germany
Job Summary
Develop and apply advanced computational and statistical methods, including machine learning, AI, and simulation approaches, to inform design, analysis, and decision-making across drug development. Build robust, reusable tools and workflows that integrate these methods into routine use while partnering with cross-functional colleagues to translate complex approaches into clear insights. Contribute to internal capability building by sharing tools and code, and support the group's external profile through scientific publications and conference presentations. This role requires a doctoral degree in statistics or related fields, three years of pharmaceutical industry experience, and strong proficiency in R and Python. The position is hybrid with 40% office time and is located in Braine l'Alleud, Monheim, Slough, or Raleigh.
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, must meet performance standards and 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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