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 Relation is offering an outstanding opportunity for a Senior Data Scientist to help build the next generation of generative and predictive models of cellular behaviour, with a focus on single cell multi-omics. Your work will be central to our mission to understand and control cellular decision-making, enabling novel therapeutic strategies grounded in generative models. You'll be joining a team with access to cutting-edge multiomic and interventional datasets, advanced computational infrastructure, and deep interdisciplinary expertise, and a culture that embraces modern ML tooling, including agentic workflows, to accelerate research iteration. You will contribute advanced data analysis and domain expertise to challenge ML models that aim to predict and explain cellular decision-making in disease. Day to day, you will Process and QC paired single-cell RNA-seq and scATAC-seq at scale from thousands of perturbations across diverse cell types and biological contexts. Develop and maintain Nextflow pipelines compatible with custom combinatorial indexing chemistries and including methods such as STARsolo, Alevin-fry, Chromap, and SnapATAC2. Integrate scRNA-seq and scATAC-seq with extracellular proteomics and high-content imaging to build multimodal training datasets. Manage data in memory-efficient formats (e.g. Zarr) for GPU-accelerated processing and model training at scale. Partner closely with wet lab scientists from screen and cohort design through to interpretation, so experiments are analytically tractable and pipelines maximise data value. Contribute to the evaluation framework that determines whether ML models capture meaningful biology: design metrics, held-out benchmarks, and biological sanity checks. Collaborate closely with ML modellers to develop model architectures. Present findings and methodologies to internal stakeholders and contribute to publications. Professionally, you will have A PhD (or equivalent industry experience) in computational biology, bioinformatics, or a related quantitative field. Deep hands-on experience processing and analysing large-scale single-cell multiomics data, ideally including paired RNA + ATAC (10x Multiome or equivalent). Proficiency with single-cell analysis toolkits (e.g. scverse ecosystem) and workflow management frameworks (Nextflow, Snakemake). Experience with arrayed CRISPR perturbation screen data, including well-level perturbation assignment via sample hashing, perturbation-specific QC (e.g. MOI estimation), and single-cell effect quantification. Proficiency in Python and familiarity with high-performance computing environments. Strong communication skills to bridge experimental and ML teams across a fast-moving programme