Director, Machine Learning & Data Science

Director, Data Science - Computational Genomics Location: London, UK (hybrid) Relation is an end-to-end biotech 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 directly from patient tissue, functional assays, and machine learning to drive disease understanding—from cause to cure. Our state-of-the-art wet and dry laboratories, located in the heart of London, provide an exceptional environment to foster interdisciplinarity and turn groundbreaking ideas into impactful therapies for patients. We are committed to building diverse and inclusive teams. Relation is an equal opportunities employer and does not discriminate on the grounds of gender, sexual orientation, marital or civil partner status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability, or age. We cultivate innovation through collaboration, empowering every team member to do their best work and reach their highest potential. By joining Relation, you will become part of an exceptionally talented team with extraordinary leverage to advance the field of drug discovery. Your work will shape our culture, strategic direction, and, most importantly, impact patients’ lives. We’re now hiring a Director of Statistical Genetics & Functional Genomics to lead a growing team that integrates human genetics, functional genomics, and computational methods to drive novel target discovery. This is a rare opportunity to help shape our therapeutic pipeline by unlocking the causal mechanisms of disease from human data. Design and implement integrative analysis frameworks combining GWAS, PheWAS, and fine-mapping with functional genomic datasets (e.G. Leverage single-cell and spatial transcriptomics to map disease-relevant cellular states and tissue-specific regulatory architecture. Foster a culture of scientific rigour, collaboration, and innovation—while mentoring junior scientists and supporting their continued development. You bring deep expertise in human statistical genetics, including fine-mapping, LD structure, polygenic risk scoring, partitioned heritability, and causal inference methods (e.G. You have experience integrating multi-modal data—including GWAS, transcriptomic (bulk and single-cell), epigenomic (ATAC-seq, ChIP-seq), and proteomic datasets—to identify causal genes and pathways. You’re fluent in Python, R, or similar languages, and comfortable working in a high-performance computing environment. You’ve led scientific projects and teams, and enjoy mentoring others while setting a high bar for excellence. Experience developing or applying machine learning or Bayesian approaches for variant-to-function inference is a plus. Join us in this exciting role, where your contributions will directly impact advancing our understanding of genetics and disease risk, supporting our mission to deliver transformative medicines to patients. Together, we’re not just conducting research—we’re setting new standards in the fields of machine learning and genetics. Resumes should not be forwarded to our job aliases or employees. Relation will not be liable for any fees associated with unsolicited CVs. #