Principal Computational Methodologist (UK)

UCB S.A.; Reading; Hybrid; GBP 85,000 - 120,000; Full time

Job summary

UCB is seeking a Principal Computational Methodologist to join our Advanced Methods and Data Science team, based in Brussels (Belgium), Slough (UK), or Raleigh (US). You will shape how AI, ML, and computational methods support clinical development, biomarker discovery, operational efficiency, and patient experience.

You will guide scientific thinking and build scalable tools that meet scientific, technical, and regulatory expectations.

Qualifications

• PhD in Statistics, Computer Science, Engineering, Computational Biology, Statistical Genetics, Mathematics or related field.
• Industry experience in pharma, clinical development, life sciences, or regulated data environments is preferred.
• Proficient in Python or R and able to guide others in practical coding.
• Strong knowledge of supervised, unsupervised, Bayesian, and deep learning methods and their real-world applications.
• Experience with ML infrastructure, reproducible research, Linux/HPC environments, and software or platform development.
• Excellent communication with scientific, technical, medical, and business partners in a collaborative, multidisciplinary team.

Responsibilities

• Lead AI and ML projects addressing clinical development, biomarker, operational, and decision-support needs.
• Design, build, test, and refine modern deep learning models for text, graphs, biological sequences, and clinical data.
• Develop generative AI applications using foundation models and large language models.
• Run rigorous, reproducible AI experiments on internal and public datasets to generate publishable results.
• Partner with engineering teams to package methods into applications, platforms, and scalable validated solutions.
• Mentor colleagues, share knowledge, and represent the team through publications, presentations, and external collaborations.

Skills

Python

R

Deep learning

Bayesian methods

Communication

Education

PhD in Statistics/CS/Engineering/Computational Biology/Genetics/Math

Tools

Linux/HPC

ML infrastructure

Software development

Job description

governance, and compliance colleagues across countries. You will also connect with academic, technology, and industry partners. Together, you will explore new methods, share scientific learning, and support teams that use data to improve decision-making across the drug development value chain.

What you will do

• You will lead AI and machine learning projects that address clinical development, biomarker, operational, and decision-support needs.
• You will design, build, test, and refine modern deep learning models for text, graphs, biological sequences, and clinical data.
• You will develop generative AI applications using foundation models, pre-trained models, and large language models.
• You will run rigorous, reproducible AI experiments on internal and public datasets to generate publishable scientific results.
• You will partner with engineering teams to package methods into applications, platforms, and scalable validated solutions.
• You will mentor colleagues, share knowledge, and represent the team through publications, presentations, and external collaborations.

Interested? For this role, we are looking for the following education, experience, and skills:

• You have a PhD in Statistics, Computer Science, Engineering, Computational Biology, Statistical Genetics, Mathematics, or a related scientific field.
• You bring experience in industry, ideally within pharma, clinical development, life sciences, or another regulated data-focused environment.
• You can program confidently in Python, R, or similar languages, and you can support others with practical coding guidance.
• You understand supervised, unsupervised, Bayesian, and deep learning methods, and can apply them to real-world scientific questions.
• You have experience with ML infrastructure, reproducible research, Linux or HPC environments, and software or platform development.
• You communicate clearly with scientific, technical, medical, and business partners, and enjoy working in a collaborative, multidisciplinary team.

Are you, ready to 'go

Make your mark for patients

We are looking for a Principal Computational Methodologist who is curious, collaborative, and impact-driven to join our Advanced Methods and Data Science team within Biometrics and Data Science, based in any of our Brussels (Belgium), Slough (UK) or Raleigh (US).

About the role

You will help shape how advanced AI, machine learning, and computational methods support clinical development. You will design, validate, and deploy models that help teams make evidence-based decisions in areas such as biomarker discovery, clinical trial design, operational efficiency, and patient experience. You will guide scientific thinking, turn complex methods into practical solutions, and help build scalable tools that meet scientific, technical, and regulatory expectations.

Who you will work with

You will join Advanced Methods and Data Science, part of Biometrics and Data Science within Patient Evidence. You will work with clinical, statistical, molecular, operational, engineering, IT, beyond' to create value and make your mark for patients, ? If this sounds like you, then we would love to hear from you!

About us

UCB is a global biopharmaceutical company, focusing on neurology and immunology. We are over 9.000 people in all four corners of the globe, inspired by patients and driven by science.

Why work with us?

At UCB, we don't just complete tasks, we create value. We aren't afraid to push forward, collaborate, and innovate to make our mark for patients. We have a caring, supportive culture where everyone feels included, respected, and has equal opportunities to do their best work. We 'go beyond' to create value for our patients, and always with a human focus, whether that's on our patients, our employees, or our planet. Working for us, you will discover a place where you can grow, and have the freedom to carve your own career path to achieve your full potential. At UCB, we've embraced a hybrid-first approach to work, bringing teams together in local hubs to