Principal Scientist, Machine Learning

We are committed to creating the most interdisciplinary team to discover and develop transformational medicines. Principal Scientist - Functional Genomics Full time Principal Scientist - Functional Genomics Relationis an end-to-end biotech 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. By combining our cutting-edge ML capabilities with their deep expertise in drug discovery, this partnership underscores our commitment to pioneering science and delivering impactful therapies to patients. 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 positive impact for patients. We are committed to building diverse and inclusive teams.Relationis an equal opportunities employer and does not discriminate on the grounds of gender, sexual orientation,maritalor 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 develop to their highest potential. By joiningRelation, you will be 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 Relation has an excellent opportunity for a Principal Scientist to join an interdisciplinary, highly collaborative team of experimental and computational drug discovery researchers in our newly built state-of-the-art functional genomics lab in London. Large-scale genetic studies, including genome-wide association studies (GWAS), have identified thousands of variants and loci associated with complex diseases. At Relation, we generate proprietary large-scale datasets to accelerate the prediction of disease genes from these loci using state-of-the-art machine learning methods. We identify the key effector genes, cells, and pathways involved in disease pathophysiology to build disease-relevant assays and validate ML platform predictions, identifying candidate targets for further validation. By joining Relation, you will be part of an exceptionally talented team, gain exposure to a broad range of skills beyond your area of expertise, help shape our culture and strategic direction, and ultimately, make a positive impact on patients’ lives. A PhD with at least 8 years of experience and an outstanding track record in academia, biotech or pharma. At least four years of hands-on experience leading the generation of large-scale high quality, screening data in arrayed format, incorporating liquid handling or automation where relevant. At least two years of hands-on research experience applying the latest CRISPR/Cas genome-editing approaches, including nucleofection and/or lentiviral transductions. Experience with high content imaging-based high throughput screens and/or FACS-based pooled screens. Experience of interdisciplinary team-working, especially with computational scientists. Broad expertise in molecular biology including molecular cloning and cell line engineering. A willingness to operate within start-up environments, including delivering to tight timelines, the ability to react to changing priorities or plans and to help with laboratory operations. An ability to communicate effectively with scientists from multiple disciplines. A desire to work within a fast-paced, data science-first environment. This is an exciting opportunity to be part of a company that is redefining how we understand genetics and disease risk. Together, we’re not just conducting research—we’re setting new standards in machine learning and genetics.