Relation is a London-based, technology-enabled biopharmaceutical company using AI-driven experimental systems to develop medicines for immunology, metabolic and bone diseases.
About Relation Relation is a sector defining TechBio company developing transformational medicines, with technology at our core. Our ambition is to understand human biology in unprecedented ways, discovering therapies to treat some of life’s most devastating diseases. We leverage single-cell multi-omics from patient tissue, functional assays, and machine learning to drive disease understanding, from cause to cure. We are scaling rapidly and building a team of exceptional individuals to push the boundaries of drug discovery. You will work in highly interdisciplinary teams where biology, computation, and engineering come together to solve complex problems that have not been solved before. Our state-of-the-art wet and dry labs in the heart of London are designed to accelerate this integration and translate insight into impact. We are committed to building diverse and inclusive teams. Relation is an equal opportunities employer and does not discriminate on the basis of gender, sexual orientation, marital or civil partnership status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability, or age. By joining Relation, you will help define how medicines are discovered and deliver meaningful impact for patients. The opportunity Relation is offering an outstanding opportunity for a Data Scientist/Senior Scientist to support and shape statistical and population genomics efforts to accelerate target identification and validation across multiple therapeutic areas. You will work with large-scale human genetics resources (e.g. biobanks and population cohorts), internally generated data, and apply cutting-edge statistical genetics methodologies to generate actionable insights. You will operate at the interface of human genetics, computational biology, and machine learning, translating genetic evidence and multi-omics into target prioritisation frameworks and mechanistic hypotheses. You will play a key role in developing robust, scalable analysis pipelines and ensuring genetic insights are integrated into decision-making across the organisation. Day to day you will, - Perform statistical and population genomics analyses using large-scale datasets to support target discovery and validation. - Design and implement statistical genetics methodologies for target prioritisation, including approaches leveraging GWAS, fine-mapping, colocalisation, MR, polygenic risk, rare variant analyses, and functional annotation. - Develop scalable computational workflows for reproducible genetics analysis, enabling robust and efficient delivery across multiple programmes. - Integrate human genetics evidence with multi-omics datasets (e.g. transcriptomics, proteomics) to uncover disease mechanisms and prioritise actionable targets. - Partner closely with experimental, translational, and ML teams to validate hypotheses, interpret findings, and guide downstream decision-making. - Communicate results clearly and confidently to internal stakeholders, including presenting methods, results, risks/limitations, and recommendations. - Contribute to publications, scientific communications, and project documentation, supporting scientific excellence and external visibility. Professionally, you will have, - PhD in statistical genetics, genomics, computational biology, bioinformatics, or a related quantitative field. - Post-PhD experience is desiderable, ideally including time in an industry, biotech, or pharmaceutical environment. - Deep expertise in statistical genetics and population genomics, including experience with large-scale human genetic datasets and post-GWAS analyses. - High proficiency in Python (preferred) and R, with experience working in high-performance computing environments. - Ability to operate independently, providing technical expertise and driving projects from concept through delivery. - Bonus experience: - Familiarity with single-cell transcriptomics or patient-derived datasets. - Experience working in interdisciplinary teams within biotech or pharma settings. - Knowledge of machine learning techniques applied to biological data. - Understanding of the end-to-end drug discovery process and how genetic evidence informs decision-making. Personally, you: - Are comfortable working in a matrixed environment, balancing multiple stakeholders and contributing effectively across teams. - Take ownership of your work, proactively seek opportunities to contribute, and enable others to do their best work. - Communicate openly and directly, give and receive feedback constructively, and handle challenging conversations with respect. - Actively seek out diverse perspectives, build strong working relationships, and contribute to shared goals across teams. - Embrace challenges with openness and resilience, set high standards for yourself, and strive to deliver meaningful outcomes. Working Style & Culture at Relation At Relation, we operate in a matrixed, interdisciplinary environment, where impact is driven through collaboration across scientific, technical, and operational domains. We collaborate, and you will partner with colleagues across multiple teams and projects, contributing your expertise while aligning to shared company priorities. We work together and win together! The patient is waiting! RECRUITMENT AGENCIES: Please note that Relation does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation will not be liable for any fees associated with unsolicited CVs. Relation is a committed equal opportunities employer.