This role sits within a technology-driven organisation advancing genomic medicine through the integration of experimental biology and computational approaches. The focus is on translating innovative platform capabilities into robust, non-clinical strategies that support the development of next-generation gene and cell therapies.

As a Senior Scientist in Translational Science, you will provide scientific leadership across non-clinical research activities, shaping study design, execution, and interpretation to enable informed programme decisions and portfolio progression.

This Will Offer You

• A senior scientific role with direct influence over translational strategy and non-clinical direction
• Ownership of preclinical study design supporting go / no-go decisions
• Close collaboration with computational biology, platform scientists, and external partners
• Exposure to cutting-edge vector technologies and data-driven development approaches
• The opportunity to shape best practices in a fast-moving, high-impact therapeutic environment

Your Responsibilities

• Design, plan, and oversee non-clinical studies validating vectors, molecular libraries, and expression systems
• Ensure scientific rigor, translational relevance, and data quality across in vitro and in vivo work
• Partner with computational scientists to integrate in silico insights into experimental design and interpretation
• Manage and coordinate CROs and external research partners
• Analyse, interpret, and communicate experimental results to inform strategic decisions
• Stay current with advances in gene and cell therapy delivery and propose innovative approaches

You Will Bring

• PhD (or equivalent industry experience) in Molecular Biology or a related field, with 5+ years of relevant industry experience
• Strong experience designing and interpreting non-clinical studies in gene therapy or related modalities
• Sound scientific judgment and confidence making data-driven recommendations and trade-offs
• Experience working with external partners and CROs, ensuring quality and delivery against timelines
• Ability to operate autonomously while collaborating effectively across disciplines
• Exposure to computational or data-driven biology approaches is advantageous, though not essential