Data Scientist, Computational Genomics/DNA Modelling

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., This is a unique opportunity for a senior data scientist to work on DNA sequence modelling, statistical machine learning, and functional genomics in support of target discovery and mechanism elucidation. You will develop and apply computational models that connect genetic variation and regulatory DNA sequence to downstream molecular phenotypes, cellular programmes, and disease biology. Embedded within the Rosalind team you will build and evaluate models that link sequence to function - including variant effect prediction,regulatory activity modelling and representation learning for genomic data.

Day to Day you will:

• Develop and implement machine learning models for DNA sequence, regulatory elements, and genetic variation in disease-relevant contexts.
• Build, evaluate and benchmark models for sequence-to-function tasks such as variant effect prediction, regulatory activity prediction, and the interpretation of non-coding disease signals.
• Integrate sequence-derived representations with transcriptomic, epigenomic, and perturbational datasets to uncover disease mechanisms and support target prioritisation.
• 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., * 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!

The patient is waiting!

RECRUITMENT AGENCIES: Please note that Relation does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation will not be liable for any fees associated with unsolicited CVs.

• PhD in computational biology, machine learning, statistics, genomics, bioinformatics, or a related quantitative discipline.
• Post-PhD experience, ideally including time in an industry, biotech, or pharmaceutical environment.
• Experience with DNA language models, genomic foundation models, or transformer-based sequence models.
• Understanding of statistical machine learning and probabilistic modelling, with experience selecting and evaluating appropriate modelling approaches for complex biological datasets.
• Knowledge of statistical genetics methods (e.g fine-mapping, colocalisation,, or variant to gene approaches).
• High proficiency in Python (preferred) and R, with experience working in high-performance computing environments.
• Ability to operate independently, driving projects from concept through delivery.
• Bonus experience:
• Familiarity with single-cell transcriptomics or patient-derived datasets.
• Experience working in interdisciplinary teams within biotech or pharma settings.
• Experience integrating machine learning models with genomics, single-cell, or perturbation datasets.
• Familiarity with 3D genome / chromatin interaction data where relevant (Hi-C, Capture-C, etc.).