Principal Statistical Methodologist (Belgium)

UCB; Brussel; Sur place; EUR 110 000 - 140 000; Plein temps

Résumé du poste

UCB is seeking a Principal Statistical Methodologist to join the Statistical Innovation team within Biometric and Data Sciences, based in our offices in Belgium, Germany, the UK or the US.

You will shape how evidence is generated and modeled across the drug development lifecycle, applying advanced data‑driven and model‑based methods (statistics, ML, AI) and translating them into robust, reusable tools that inform decision making.

Qualifications

• Doctoral degree in statistics, biostatistics, mathematics, computer science with strong quantitative component.
• 3+ years of experience in the pharmaceutical industry; clinical development experience advantageous.
• Strong programming skills in R and Python; familiarity with version control, testing, and reproducible workflows.
• Demonstrated expertise in machine learning/AI methods; experience with ML/AI in real problems.

Responsabilités

• Develop and apply advanced computational and statistical methods, including ML/AI, to inform design, analysis, and decisions across development.
• Build robust, reusable tools and workflows for routine use with reproducible software quality.
• Explore synthetic data, causal inference, digital-twin, and simulation approaches where relevant.
• Collaborate with statisticians and cross‑functional teams to translate complex methods into actionable insights.

Connaissances

R

Python

Machine learning

AI methods

Statistical inference

English communication

Formation

Doctoral degree in statistics or related field

Outils

R

Python

Git

Jupyter

Description du poste

Principal Statistical Methodologist

We are looking for a Principal Statistical Methodologist who is curious, collaborative, and strategic to join our Statistical Innovation team within Biometric and Data Sciences (BDS), based in any of our Braine l'Alleud (Belgium), Monheim (Germany), Slough (UK) or Raleigh (US) offices.

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, 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.

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. Experience in advanced computational methodology for clinical development (early to late stage) is an advantage. Direct entry may be considered.
• 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.

Equal Opportunity Employer

UCB is an equal opportunity employer. All employment decisions will be made without regard to any characteristic protected by applicable laws.

Reasonable Accommodation

Should you require any adjustments to our process to assist you in demonstrating your strengths and capabilities, contact us at [e-mail]. Please note that if your enquiry does not relate to adjustments, we will not be able to support you through this channel.