Data Scientist – Computational Genomics, 12-month FTC

About Relation

Relation is a sector defining TechBio 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.

The opportunity

This is a unique opportunity for a Data Scientist to bridge the gap between computational genomics and machine learning at scale. Operating at the genomics-ML interface, you will shape our computational genomics efforts to accelerate target identification and validation across diverse therapeutic areas, leveraging large-scale human genetics resources — genetic discovery, biobanks, OMICs, single-cell atlases and other internal datasets— to gain actionable insight. By building, refining and deploying cutting-edge ML‑focussed methods you will inform robust functional prioritisation frameworks, mechanistic hypotheses, and strategic decision‑making across the organisation.

Day to Day you will:

• Apply, build, refine and integrate statistical models to gain insight from genomics, transcriptomics and other OMICs datasets and support target discovery and validation.
• Work cross‑functionally at the ML‑genetics interface to identify opportunities, solve problems and implement solutions for shared insight.
• Integrate human genetics evidence with OMICs datasets (e.g. transcriptomics, proteomics) to uncover disease mechanisms and prioritise actionable targets.
• Develop scalable computational workflows for reproducible analysis within Relation’s existing stack.
• Partner closely with experimental and machine learning researchers to validate hypotheses, interpret results, and guide downstream studies.
• Communicate findings clearly to internal stakeholders, including presenting methods, results, and recommendations.
• Contribute to publications, scientific communications, and project documentation, supporting scientific excellence and external visibility.

Professionally, you will have:

• PhD in statistical genetics, genomics, computational biology, machine learning, bioinformatics, or a related quantitative field.
• Knowledge of machine learning techniques applied to biological data.
• Experience in quantitative genomics, statistics, bioinformatics, or multi‑omics data analysis.
• Proficiency in Python (preferred), or R, and familiarity with high‑performance computing environments, collaborative coding and version control (e.g. git).

Bonus experience:

• Familiarity with single‑cell transcriptomics or patient‑derived datasets.
• Experience working in interdisciplinary/matrixed teams within biotech or pharma settings.
• Understanding of the end‑to‑end drug discovery process and how genetic evidence informs decision‑making.

Personally, you:

• 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.

Working Style & Culture at Relation

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 colleagues across multiple teams and projects, contributing your expertise while aligning to shared company priorities. We work together and win together!