Senior/Principal Data Scientist - Single Cell & Spatial

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 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 partnership 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.

The Opportunity

This is a unique opportunity for a data scientist to work on multi‑omics and perturbational data to drive transformative insights into drug discovery. As a member of the Single Cell and Spatial Omics team, and embedded within one of the ML research teams, you will contribute advanced data analysis and domain expertise to challenge ML models that aim to predict and explain cellular decision‑making in disease.

Your Responsibilities

• Develop and implement computational workflows for integrating and analysing multi‑omics data.
• Design statistical models for analysing transcriptomics and other omics datasets.
• Use domain and data insights to design meaningful and challenging evaluation tasks for ML models.
• Collaborate closely with ML modellers to develop model architectures.
• Work with experimental teams to design and validate computational hypotheses.
• Present findings and methodologies to internal stakeholders and contribute to publications.

Professionally, You Have

• A PhD in computational biology, bioinformatics, or a related quantitative field.
• Extensive experience in multi‑omics data analysis, including transcriptomics.
• Proficiency in Python and familiarity with high‑performance computing environments.

Desirable Knowledge or Experience

• Familiarity with single‑cell transcriptomics or patient‑derived datasets.
• Knowledge of ML techniques applied to biological data.
• A background in statistical modelling and algorithm development.
• Experience working in interdisciplinary teams.

Personally, You Are

• An inclusive leader and team player.
• A clear communicator.
• Driven by impact.
• Humble and eager to learn.
• Motivated and curious.
• Passionate about making a difference in patients’ lives.

Join us in this exciting role, where your contributions will directly advance 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.