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 an Senior Scientist, Biologics Drug Discovery to bring deep biologics drug discovery knowledge to our growing pipeline. You will contribute to target assessment, modality selection and biologics discovery strategies through deep technical expertise and hands-on scientific execution from early concept through to IND-enabling studies. As Relation grows its scientific and machine learning capabilities, you will act as a highly experienced scientist, bringing deep expertise in antibody discovery and engineering while contributing directly to programme execution and scientific decision-making, working alongside our computational, ML, and translational teams to translate biological insight into well-designed molecules with a credible path to the clinic. We are particularly interested in scientists who have personally designed, engineered and optimised antibody molecules, including experience with affinity maturation, CDR engineering, developability assessment and data-driven molecule optimisation. You will additionally be a key technical voice in pipeline decisions and a trusted partner to colleagues across Drug Discovery and beyond. Day to day, you will Demonstrated hands-on experience designing, engineering and optimising antibody molecules, with a strong understanding of the experimental approaches used to assess potency, selectivity, developability and mechanism of action. Ability to independently interpret complex datasets and use those data to drive molecule design and optimisation decisions. Maintain a hands-on role in biologics discovery activities while partnering closely with internal and external teams. Drive biologics programs end-to-end — from target validation and hit identification through lead characterization, optimization, and developability triage — against agreed Target Product Profiles (TPPs). Make informed modality decisions across the biologics landscape (antibodies, bispecifics, ADCs, fusion proteins, fragments) and ensure the right molecule is selected for the right target and indication. Design and oversee fit-for-purpose discovery packages, combining cell-based functional assays, biophysical characterization (SPR/Biacore, BLI/Octet), and developability assessment to drive sound candidate selection. Operate effectively in a matrixed pharma/biotech environment, partnering with Computational Biology, Machine Learning, CMC, DMPK, Toxicology, and Translational colleagues to de-risk candidates and progress them toward IND-enabling studies. Partner with external collaborators and CROs, contributing to study design, data review and scientific interpretation. Share expertise with colleagues and contribute to a collaborative scientific environment. Partner with our ML and computational biology teams to bring biologics drug discovery insight into how predictive and generative approaches are applied to target selection, molecule design, and lead optimization. Personally contribute to the design, engineering and optimisation of antibody molecules, using experimental data to guide scientific decisions and progression strategies. Contribute to Relation’s scientific culture by mentoring junior scientists in biologics drug discovery, supporting publications and conference visibility, and helping to shape the broader scientific identity of the biologics group. Professionally, you will have A PhD. in Molecular Biology, Immunology, Pharmacology, Cell Biology, Protein Engineering, or a related life-science discipline. Hands-on drug discovery experience gained in a pharma or biotech setting, with a clear track record of contributing to biologics programs progressing through key discovery milestones (target validation, hit-to-lead, lead optimization, candidate selection). Proven experience working in a matrixed pharma/biotech environment, partnering effectively across functions (e.g. CMC, DMPK, Toxicology, Computational/ML, Translational) and balancing multiple stakeholders. Deep working knowledge of biologics drug discovery: how biology is translated into a molecule, how modalities are chosen, how discovery cascades are designed, and what data are needed at each stage to make sound progression decisions. Familiarity with the biologics modality landscape (antibodies, bispecifics, ADCs, fusion proteins, fragments) and an informed view of when each modality is the right tool for the biology. Hands-on experience with the data types that drive biologics drug discovery decisions, including cell-based functional and screening assays and biophysical characterization (SPR/Biacore, BLI/Octet). Working experience of CRO management across biologics drug discovery activities (e.g. molecule production, in vitro pharmacology, in vivo studies). A good understanding of biologics drug discovery end-to-end, including TPP-driven decision-making, developability considerations, and awareness of what is required to support IND-enabling activities. Curiosity about how ML and computational approaches can complement traditional biologics discovery workflows, and an interest in shaping how those tools are deployed.