Data Scientist – Computational Genomics

About the role

This role is a 12-month fixed-term contract for a Data Scientist to bridge the gap between computational genomics and machine learning. You will shape computational genomics efforts to accelerate target identification and validation across diverse therapeutic areas by leveraging large-scale human genetics resources. You will build, refine, and deploy machine learning methods to inform functional prioritisation frameworks and mechanistic hypotheses.

Responsibilities

• 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 to uncover disease mechanisms and prioritise actionable targets.
• Develop scalable computational workflows for reproducible analysis within the 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.

Required skills

• Statistical genetics
• Genomics
• Computational biology
• Machine learning
• Bioinformatics
• Python
• R
• High-performance computing
• Git

Nice to have

• Single-cell transcriptomics
• Patient-derived datasets
• Drug discovery

Qualifications

• PhD in statistical genetics, genomics, computational biology, machine learning, bioinformatics, or a related quantitative field

About the Company

Relation is a sector defining TechBio company developing transformational medicines, with technology at its core. The company leverages single-cell multi-omics from patient tissue, functional assays, and machine learning to drive disease understanding.