Computational Scientist (AI & Biology)

Ensocell is a venture-backed biotechnology company combining single-cell and spatial genomics, AI, and translational biology to identify novel therapeutic targets and develop next-generation precision medicines. Our ENSIGHT platform utilises integration of large-scale human datasets, machine learning, and experimental biology to uncover disease mechanisms and therapeutic intervention points with unprecedented resolution. We are building an interdisciplinary team spanning computational biology, AI/ML, software engineering, and experimental biology, united by a shared ambition to transform how therapeutics are discovered. The opportunity We are seeking a Computational Scientist to join Ensocell at the intersection of single-cell biology, spatial genomics, and AI-driven therapeutic discovery. In this role, you will work across platform development and translational discovery projects, applying computational and machine learning approaches to large-scale biological datasets to uncover disease mechanisms and assess therapeutic opportunities. You will work closely with computational biologists, AI/ML scientists, and experimental researchers to evaluate, challenge, and improve emerging foundation models and AI systems for biology. The role combines deep biological data analysis with method evaluation and model development, making it ideal for someone who enjoys both rigorous computational work and high-impact scientific application. The role may be appointed at Scientist or Senior Scientist level, depending on the candidate’s academic and industry experience. This is an opportunity to help shape how AI and large-scale biological data are integrated into next-generation drug discovery in a highly collaborative and fast-moving environment. We offer a competitive package and strong career development opportunities, with scope for scientific ownership and impact in a fast-paced biotech. What you will do * Analyse and interpret single-cell, spatial, and multi-omics datasets to uncover disease mechanisms and therapeutic hypotheses. * Evaluate, benchmark, and apply foundation models and machine learning approaches for biological data analysis. * Develop scalable computational workflows and reproducible analysis pipelines for high-dimensional biological datasets. * Collaborate closely with AI/ML scientists to improve model evaluation, biological interpretation, and downstream applications. * Support translational and target discovery projects through exploratory and hypothesis-driven analyses. * Partner with experimental scientists to design analyses, interpret results, and prioritise follow-up studies. * Contribute to internal standards and best practices for computational biology, benchmarking, and reproducible research. * Communicate findings clearly to interdisciplinary teams. What we are looking for * A PhD in computational biology, bioinformatics, machine learning, statistics, physics, mathematics, computer science, or a related quantitative discipline. * Strong experience analysing high-dimensional biological data, particularly single-cell and/or spatial omics datasets. * High proficiency in Python and experience working in cloud or high-performance computing environments. * Experience developing or applying machine learning methods to biological datasets. * Familiarity with modern ML frameworks such as PyTorch or JAX. * Strong understanding of statistical modelling and computational data analysis. * Experience working with multimodal biological datasets is highly desirable. * Experience evaluating or benchmarking AI models for biological applications is desirable. * Experience working in industry is desirable. Benefits (what we offer) We offer a competitive package, which typically includes: * Competitive salary and equity participation * Enhanced employer pension contribution * Generous annual leave plus UK bank holidays * Private Medical, Income Protection, Critical Illness, Employee Assistance Plan * Training and development support * A collaborative, science-led culture at Babraham Research Campus.