Senior/Principal Machine Learning Scientist Causality

LondonRelation 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 lifes most devastating diseases. We leverage single-cell multi-omics directly from patient tissue, functional assays, and machine learning (ML) to drive disease understanding - from cause to cure.By combining our cutting-edge ML capabilities with GSKs 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 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 are seeking an exceptional Machine Learning Scientist with expertise in causal inference to help build the next generation of predictive, mechanism-aware models of cellular behaviour. This is an opportunity to push the boundaries of what causal modelling can achieve in complex, high-dimensional, and noisy real-world systems, and to see your work tested directly in experimental biology.

Design and implement causal learning approaches that capture regulatory logic, cell fate trajectories, and intervention effects from diverse biological data, including single-cell perturbation experiments.Collaborate with experimental teams to design and validate computational hypotheses via iterative strategies that inform or guide the next experiment (lab-in-the-loop).PhD in ML, statistics, computer science or a related quantitative field.Strong background in one or more of probabilistic modelling, time series analysis, or dynamical systems.Proficiency in Python and familiarity with scalable ML tooling and high-performance computing.Familiarity with biological datasets, particularly single cell and perturbational data.Track record of impactful publications or open-source contributions in ML.Experience working in interdisciplinary teams or applying ML in real world settings.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, were not just conducting researchwere setting new standards in the fields of ML 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.#