Overview
In this Scientist role, you will develop novel computational methods to translate sequencing data into actionable biology insights and support new product development. You will work within a multidisciplinary team applying advanced analytics to large-scale datasets in genomics and healthcare. The position emphasizes methods development—creating new algorithms and ML/AI approaches to drive real-world impact. This hybrid, permanent role offers collaboration with researchers and engineers, with opportunities to shape scalable workflows and be at the forefront of translational science.
Pay / Benefits
- competitive salary and package
- bonus
- hybrid working
- flexible working patterns
- career progression opportunities
- significant compute resources
Responsibilities
- Design and build novel algorithms for genomics, sequencing, and large-scale data analysis
- Develop and evaluate machine learning models for complex scientific questions
- Validate and benchmark new computational methods on public and proprietary datasets
- Collaborate with bioinformaticians and software engineers to deploy methods in scalable workflows
- Cross-team collaboration to translate research into practical applications
- Own scientific projects from concept to delivery
Key requirements
- Strong quantitative background with experience creating new computational methods
- MSc or PhD (completed or near completion) in Computational Biology, Computer Science, Mathematics, Statistics, Physics, ML, Bioinformatics, or related field
- Experience developing novel algorithms or ML models, preferably in genomics or large-scale biological data
- Solid programming skills in Python; knowledge of other languages (C/C++, Julia, R, Rust) beneficial
- Experience delivering computational tools, models, or analytical methods
- Experience with sequencing data, cloud computing, workflows, ML Ops, or large-scale data infrastructure is advantageous
- Interest in leading projects
- leadership potential
- collaboration
- problem-solving
- Python
- machine learning / ML
- genomics / sequencing data
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