About the role
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 This is a unique opportunity for a senior data scientist to work on DNA sequence modelling, statistical machine learning, and functional genomics in support of target discovery and mechanism elucidation. You will develop and apply computational models that connect genetic variation and regulatory DNA sequence to downstream molecular phenotypes, cellular programmes, and disease biology. Embedded within the Rosalind team you will build and evaluate models that link sequence to function - including variant effect prediction,regulatory activity modelling and representation learning for genomic data. Day to Day you will: Develop and implement machine learning models for DNA sequence, regulatory elements, and genetic variation in disease-relevant contexts. Build, evaluate and benchmark models for sequence-to-function tasks such as variant effect prediction, regulatory activity prediction, and the interpretation of non-coding disease signals. Integrate sequence-derived representations with transcriptomic, epigenomic, and perturbational datasets to uncover disease mechanisms and support target prioritisation. Partner closely with experimental and machine learning researchers to validate hypotheses, interpret results, and guide downstream studies. Communicate findings clearly to internal stakeholders, including presenting methods, results, and recommendations. Contribute to publications, scientific communications, and project documentation, supporting scientific excellence and external visibility. Professionally, you will have PhD in computational biology, machine learning, statistics, genomics, bioinformatics, or a related quantitative discipline. Post-PhD experience, ideally including time in an industry, biotech, or pharmaceutical environment. Experience with DNA language models, genomic foundation models, or transformer-based sequence models. Understanding of statistical machine learning and probabilistic modelling, with experience selecting and evaluating appropriate modelling approaches for complex biological datasets. Knowledge of statistical genetics methods (e.g fine-mapping, colocalisation,, or variant to gene approaches). High proficiency in Python (preferred) and R, with experience working in high-performance computing environments. Ability to operate independently, driving projects from concept through delivery.