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Senior/Principal Machine Learning Scientist – Causality

Relation
London
1 month ago
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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 life’s 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 life’s 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 GSK’s 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. You’ll 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.

Professionally, you have

  • 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.

Desirable Knowledge Or Experiences

  • 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.

Personally, you are

  • 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.

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, we’re not just conducting research—we’re setting new standards in the fields of ML and genetics. The patient is waiting!

Relation is a committed equal opportunities employer.

RECRUITMENT AGENCIES: Please note that Relation does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation will not be liable for any fees associated with unsolicited CVs.

Seniority level

  • Seniority levelMid-Senior level

Employment type

  • Employment typeFull-time

Job function

  • Job functionOther

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