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Job description 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