Senior Data Scientist (Imaging

About Relation

Relation is a sector defining Tech Bio 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 from patient tissue, functional assays, and machine learning to drive disease understanding, from cause to cure.

We are scaling rapidly and building a team of exceptional individuals to push the boundaries of drug discovery. You will work in highly interdisciplinary teams where biology, computation, and engineering come together to solve complex problems that have not been solved before. Our state-of-the-art wet and dry labs in the heart of London are designed to accelerate this integration and translate insight into impact.

We are committed to building diverse and inclusive teams. Relation is an equal opportunities employer and does not discriminate on the basis of gender, sexual orientation, marital or civil partnership status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability, or age.

By joining Relation, you will help define how medicines are discovered and deliver meaningful impact for patients.

Relation is offering an outstanding opportunity for a Senior Data Scientist to help build the next generation of generative and predictive models of cellular behaviour, with a focus on high-content imaging. Your work will be central to our mission to understand and control cellular decision-making, enabling novel therapeutic strategies grounded in generative models.

You'll be joining a team with access to cutting-edge multiomic and interventional datasets, advanced computational infrastructure, and deep interdisciplinary expertise, and a culture that embraces modern ML tooling, including agentic workflows, to accelerate research iteration.

The role exists to drive imaging data science: curate training-ready imaging datasets, design evaluation that distinguishes biological signal from technical artefact, interpret foundation-model behaviour on cellular imaging, and provide the wet lab interface for the imaging assay teams that generate the data.

• Curate training-ready imaging datasets across cell painting and brightfield assays.

• Drive evaluation design for imaging modalities in close partnership with ML and data scientists.

• Surface what models learn about cellular morphology, identify failure modes, and feed findings back into modelling decisions.

• Partner with wet lab assay teams from plate design through to data delivery, ensuring imaging assays remain analytically tractable and yield valuable data.

• Contribute to multi-modal evaluation: link imaging readouts to single-cell and perturbation data.

• Communicate methodology and findings clearly and rigorously across the computational team.

• Track developments in foundation models for biological imaging, and bring relevant advances into our work.

• A PhD (or equivalent industry experience) in computational biology, bioinformatics, computer vision, biomedical engineering, or a closely related discipline.

• Strong fluency with high-content imaging modalities, including cell painting and brightfield: knowledge of what good data looks like, recognition of common artefacts, and judgement on biological relevance.

• Working knowledge of deep learning approaches to imaging, sufficient to reason about model behaviour and design appropriate evaluation.

• Experience designing or contributing to evaluation frameworks for ML models, ideally with imaging or comparable scientific image data.

• Strong Python data-science stack (numpy, pandas / polars, scikit-learn, PyTorch).

• Familiarity with terabyte-scale imaging data and standard scientific formats.

• Strong biological grounding and the ability to partner effectively with wet lab scientists.

• Bonus experience: familiarity with classical bioimage tooling (Cell Profiler, scikit-image, ImageJ / Fiji) for QC reference; large public cell painting datasets; publication record in relevant venues.

• Are comfortable working in a matrixed environment,
  balancing multiple stakeholders and contributing effectively across teams.

• Take ownership of your work
  , proactively seek opportunities to contribute, and enable others to do their best work.

• Communicate openly and directly
  , give and receive feedback constructively, and handle challenging conversations with respect.

• Actively seek out diverse perspectives
  , build strong working relationships, and contribute to shared goals across teams.

• Embrace challenges with openness and resilience
  , set high standards for yourself, and strive to deliver meaningful outcomes.

At Relation, we operate in a matrixed, interdisciplinary environment
, where impact is driven through collaboration across scientific, technical, and operational domains. We collaborate, and you will partner with…