Senior Computational Biologist

About ScionxBio

ScionxBio is engineering the next generation of RNA medicines, combining synthetic chemistry and artificial intelligence to create xRNA — a platform for long-acting therapeutics. By expanding RNA beyond nature's four-letter alphabet, we engineer molecules that sustain protein expression for weeks to months rather than days, with a lower immunogenic burden and the durability that chronic disease demands. At the heart of the platform is our TERA® engine, a closed-loop system in which deep-learning design, proprietary combinatorial assembly, high-throughput screening, and continuous model retraining work together to optimise each molecule for its payload. With an initial focus on cardiometabolic disease, we are working to turn transient treatments into durable therapies. A spinout from Eleven Therapeutics, we are an international team operating across Cambridge (UK), Tel Aviv, and Cambridge (MA), with our Cambridge (UK) site serving as our largest research and development hub.

Position Summary

We are seeking a talented Computational Biologist to join our Data Sciences team at our Cambridge, UK site and play a central role in the engine that drives our platform. This is a position for someone who is energised by the interface between large experimental datasets and predictive modelling, and who wants to see their analyses translate directly into better molecules. Working hand in hand with our wet-lab, RNA technology, and chemistry colleagues, you will turn the high-throughput data our platform generates into the insights and predictive tools that shape each design–build–test–learn cycle. The ideal candidate brings a strong foundation in the analysis of in vitro high-throughput screening data, a genuine fluency in building automated and reproducible analysis pipelines, and ideally experience applying machine learning to the prediction of molecular properties such as RNA and peptide stability.

Key Responsibilities

• Design, build, and maintain automated, reproducible pipelines for the processing, quality control, and analysis of data generated by our in vitro high-throughput screening platforms, enabling fast and reliable turnaround at scale.
• Analyse and integrate large, diverse experimental datasets to draw robust scientific conclusions that inform platform development and pipeline decisions.
• Develop, train, and deploy machine-learning models to predict key molecular properties — including the stability of RNA and peptide payloads — and to prioritise candidates for experimental testing.
• Work closely with wet-lab scientists to translate experimental questions into computational approaches, and to feed predictions back into the next round of design and screening.
• Develop accessible tools and visualisations that put data and model outputs directly into the hands of bench scientists for repeated, self-service use.
• Maintain rigorous standards of data integrity, documentation, and version control, contributing to a robust and well-engineered analytical codebase.
• Stay abreast of advances in machine learning, RNA and oligonucleotide modelling, and computational drug discovery, and proactively bring promising new methods into our workflows.
• Communicate methods, results, and recommendations clearly to both technical and non-technical colleagues across our distributed, multidisciplinary teams.

Required Qualifications and Experience

• A PhD in Computational Biology, Bioinformatics, Data Science, Computer Science, or a closely related discipline; or an MSc with equivalent relevant experience, ideally including time in an industry setting.
• Demonstrable experience in the analysis of in vitro high-throughput screening data, including the practical challenges of working with large, noisy experimental datasets.
• A strong track record of building automated, reproducible data-processing and analysis pipelines.
• Strong programming skills in Python, together with sound software-development practices such as version control (Git), testing, and documentation.
• Experience developing and applying machine-learning models for predictive tasks.
• Excellent communication skills and a genuinely collaborative approach, comfortable working across culturally diverse, geographically distributed teams.

Highly Desirable Experience

• Experience applying machine learning to the prediction of molecular stability or other properties of RNA, oligonucleotides, or peptides.
• Familiarity with RNA biology, oligonucleotide chemistry, or RNA structure modelling.
• Experience working with laboratory automation and associated data management systems (e.g. LIMS).
• Experience with scientific computing in a Unix / high-performance or cloud computing environment.

Personal Attributes

• A proactive, problem-solving mindset and the intellectual curiosity to tackle open-ended scientific questions.
• Care for rigour, reproducibility, and detail in everything from code to communication.
• Adaptability and resilience in a fast-paced, evolving biotech environment.
• A collaborative spirit and a real enthusiasm for working at the interface of computation and experimental biology.

ScionxBio is an equal opportunities employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We particularly welcome applications from people who are underrepresented in the sciences.