Senior/Principal Machine Learning Scientist Causality (London)
29 Days Old
Senior/Principal Machine Learning Scientist Causality
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Senior/Principal Machine Learning Scientist Causality
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About Relation
Relation 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.London
About Relation Relation 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. This year, we embarked on an exciting dual collaboration with GSK to tackle fibrosis and osteoarthritis, while also advancing our own internal osteoporosis programme. 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. We are rapidly scaling our technology and discovery teams, offering a unique opportunity to join one of the most innovative TechBio companies. Be part of our dynamic, interdisciplinary teams, collaborating closely to redefine the boundaries of possibility in drug discovery. 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. Opportunity 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. Your work will be central to our mission to understand and control cellular decision-making, enabling novel therapeutic strategies grounded in causal and interpretable models. Youll be joining a team with access to cutting-edge multiomic and interventional datasets, advanced computational infrastructure, and deep interdisciplinary expertise. 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. Your Responsibilities- Collaborate with domain experts to translate biological hypotheses into formal causal modelling problems.
- 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.
- Develop models that go beyond correlation, focusing on generalisation, counterfactual prediction, and experimental design.
- Collaborate with experimental teams to design and validate computational hypotheses via iterative strategies that inform or guide the next experiment (lab-in-the-loop).
- Evaluate models not just for fit, but for causal coherence, mechanistic fidelity, and utility in guiding real-world interventions.
- Communicate findings clearly across disciplinary boundaries, and contribute to high-impact publications.
- PhD in ML, statistics, computer science or a related quantitative field.
- Deep expertise in causal inference, such as causal graphical models, counterfactual reasoning, or invariant representation learning.
- 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.
- Inclusive leader and team player.
- Clear communicator.
- Driven by impact.
- Humble and hungry to learn.
- Motivated and curious.
- Passionate about making a difference in patients lives.
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29 Days Old
Senior/Principal Machine Learning Scientist Causality (London)
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London
Senior/Principal Machine Learning Scientist Causality Join to apply for the Senior/Princ Principal Machine Learning scientist Causability role at Relation. Get AI-powered advice on this job and more exclusive features.
More Details -
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29 Days Old
Senior/Principal Machine Learning Scientist Causality (London)
-
London
Relation is an end-to-end biotech company developing transformational medicines. We leverage single-cell multi-omics directly from patient tissue, functional assays, and machine learning (ML) to drive disease understanding - from cause to cure. Your work will be central to our mission to understand and control cellular decision-making.
More Details -