We have an exciting opportunity within a growing technology-led organisation applying advanced machine learning to complex biological and scientific data. The focus is on translating foundational models into practical, real-world workflows that support discovery, development, and decision-making in highly technical environments.

As a Senior Computational Scientist, you will provide scientific leadership at the intersection of biology and machine learning, ensuring applied work remains grounded in real-world challenges while delivering outputs that are usable, interpretable, and impactful.

This Will Offer You

• A senior individual contributor role with clear influence over scientific direction
• Ownership of applied model evaluation and real-world use cases
• Close collaboration with research, product, and commercial-facing teams
• The opportunity to mentor junior scientists and shape best practices
• Exposure to cutting-edge ML methods applied to complex biological data
• Scope to progress into people or technical leadership as the team scales

Your Responsibilities

• Lead the application and evaluation of foundation models in real-world scientific workflows
• Ensure scientific rigor and relevance across applied modelling efforts
• Act as a key scientific partner to research and engineering teams
• Translate complex biological and ML concepts into clear, actionable insights
• Support and mentor junior team members
• Collaborate with product and business-facing teams to turn research outputs into deliverables

You Will Bring

• PhD in Computational Biology, Bioinformatics, Machine Learning, or a related field with industry experience or MSc in a relevant field with substantial industry experience applying ML to biological data
• Experience with single-cell
• Strong understanding of ML/AI methods for biological datasets (e.g. transformers, VAEs, diffusion models, classical ML)
• Hands-on experience with modern ML frameworks such as PyTorch or equivalent
• Ability to communicate complex scientific ideas to both technical and non-technical audiences
• Comfort operating in fast-paced, iterative environments
• A strong interest in building at the intersection of biology and machine learning