Associate Director - Computational Genomics
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 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.
The Opportunity
This is a unique opportunity for an Associate Director to lead statistical and population genomics strategy to accelerate target identification and validation across multiple therapeutic areas. You will be responsible for shaping how large-scale human genetics and biobank data are analysed and integrated with patient-derived multi-omics datasets to generate robust, actionable insights for drug discovery.
As part of the Cross Indication team, you will operate at the interface of human genetics, multi-omics, and machine learning, translating genetic evidence into target prioritisation frameworks and mechanistic hypotheses. You will provide scientific and technical leadership, ensuring that genetics and multi-omics insights are consistently and effectively embedded in decision-making across the organisation.
Your responsibilities
Define and lead the statistical and population genomics analyses across programmes, leveraging large-scale datasets such as biobanks, population cohorts, and multi-ancestry studies.
Design and implement statistical genetics methodologies for target prioritisation, including approaches leveraging GWAS, fine-mapping, colocalisation, polygenic risk, rare variant analyses, and functional annotation.
Ensure genetic analyses are rigorous, reproducible, and decision-relevant, with clear articulation of strengths, limitations, and uncertainty.
Lead the development of integration strategies, enabling consistent interpretation of signals across population-scale and deeply phenotyped patient datasets.
Partner closely with experimental, translational, and ML teams to validate hypotheses, interpret findings, and guide downstream decision-making.
Communicate complex analyses clearly and confidently to senior internal stakeholders, translating technical results into strategic recommendations.
Contribute to publications, scientific communications, and project documentation, supporting scientific excellence and external visibility.
Professionally, you have
PhD in statistical genetics, genomics, computational biology, bioinformatics, or a related quantitative field.
Post-PhD experience in the biotech or pharmaceutical industry is required, with demonstrated impact on drug discovery programmes.
Deep expertise in statistical genetics, population genomics, including experience with large-scale human genetic datasets and post-GWAS analyses.
High proficiency in Python (preferred) and R, with experience working in high-performance computing environments.
Ability to operate independently at a senior level, driving initiatives from concept through delivery and influencing cross-functional teams.
Desirable knowledge or experiences
Experience integrating single-cell or spatial transcriptomics with genetics and proteomics.
Track record of working effectively in highly interdisciplinary teams within biotech or pharma environments.
Knowledge of machine learning techniques applied to biological data.
Experience with causal inference frameworks (e.g. Mendelian randomisation) to strengthen target validation.
Strong understanding of the end-to-end drug discovery process and how human genetics evidence is used to prioritise and de-risk targets.
Personally, you are
Inclusive leader and team player.
Clear communicator.
Driven by impact.
Humble and hungry to learn.
Motivated and curious.
Impact-driven and passionate about improving patient outcomes.
Comfortable working in dynamic, fast-paced environments.
Join us in this exciting role where your contributions will have a direct impact on advancing our understanding of genetics and disease risk, supporting our mission to get transformative medicines to patients. Together, we're not just doing research; we're setting new standards in the field of machine learning and genetics. The patient is waiting!
Relation Therapeutics is a committed equal opportunities employer.
RECRUITMENT AGENCIES: Please note that Relation Therapeutics does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation Therapeutics will not be liable for any fees associated with unsolicited CVs.