Principal Data Scientist - Computational Biology

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 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 Tech Bio 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.

We are seeking a Principal Data Scientist, Computational Biology to join Relation, working at the intersection of single‑cell biology, spatial omics, and machine learning.

In this role, you will apply advanced computational and statistical approaches to analyse high‑dimensional biological datasets, generating insights that directly inform disease understanding and drug discovery. You will sit within the Single Cell & Spatial Omics function, working closely with ML researchers, experimental scientists, and software engineers to translate complex biological data into actionable knowledge.

This is a highly collaborative, scientifically driven role, suited to someone who enjoys working deeply with data, challenging models with biological insight, and contributing meaningfully to interdisciplinary research programmes.

• Analyse and interpret single‑cell, spatial, and other multi‑omics datasets to uncover biological mechanisms relevant to disease and therapeutic intervention.

• Develop and apply statistical and computational methods for transcriptomics and related omics data.

• Use domain expertise to design rigorous evaluation tasks that test, challenge, and refine ML models.

• Collaborate closely with ML scientists to inform model assumptions, features, and interpretation.

• Work with experimental teams to help design experiments and validate computational hypotheses.

• Clearly communicate insights, results, and methodologies to internal stakeholders and contribute to scientific publications.

• A PhD in computational biology, bioinformatics, statistics, physics, mathematics, or a related quantitative field.

• Strong experience working with high‑dimensional biological data, including transcriptomics and other omics modalities.

• Proficiency in Python, with experience in scientific computing and data analysis.

• A solid understanding of statistical modelling and quantitative methods applied to biological data.

• Experience with Bayesian models.

• Hands‑on experience with single‑cell and/or spatial omics data, including patient‑derived samples.

• Familiarity with machine learning approaches used in…