Scientist - Computational Method Development


Company:	Hays Specialist Recruitment
Salary:	Not specified
Hours:	Full-time
Location:	Oxford, OX1 4AU
Working pattern:	Hybrid - 3 days remote
Job type:	Permanent
Posting date:	17 Sept 2026
Closing date:	17 Oct 2026

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Summary

Your new company

You'll be joining an innovative research team developing next-generation technologies at the intersection of genomics, artificial intelligence, and healthcare. You'll be part of a multidisciplinary department of computational scientists, bioinformaticians, engineers, and researchers applying advanced analytics to large-scale biological datasets and real-world scientific challenges.

Your new role

As a Scientist, you will develop novel computational methods that transform complex sequencing data into actionable biological insights and support novel product development to have a real-world impact. This is a genuine methods development role, focused on creating new algorithms and ML/AI approaches rather than applying existing bioinformatics pipelines. This role can be hired at different grades, eg a recent PhD graduate or someone with a couple of years' post-doc or industry experience.

The role is permanent and hybrid working - it requires some time on site (preferably 3 days/week but potentially flexible for the right candidate).

Key responsibilities include:

Designing and developing novel algorithms for genomics, sequencing, and large-scale biological data analysis

Building and evaluating machine learning models to address complex scientific and translational research questions

Validating and benchmarking new computational methods using large public and proprietary datasets

Working closely with bioinformaticians and software engineers to deploy methods within scalable, reproducible workflows

Collaborating across computational, biological, and data science teams to translate research into practical applications

Taking ownership of scientific projects from concept through implementation, evaluation, and delivery

What you'll need to succeed

A successful candidate will combine strong quantitative skills with genuine experience developing new computational methods.You should have:

An MSc or PhD (awarded or nearly completed) or equivalent experience in Computational Biology, Computer Science, Mathematics, Statistics, Physics, Machine Learning, Bioinformatics, or a related quantitative discipline

Experience developing novel algorithms, computational methods, or machine learning models, preferably within genomics or other large-scale biological data domains

Strong understanding of data structures, algorithms, statistical modelling, and quantitative problem-solving

Very good programming skills in Python, with experience in other languages an advantage (eg C, C++, Julia, R, Rust, etc)

Experience delivering computational tools, models, or analytical methods

Experience with sequencing data, cloud computing, workflow development, ML Ops, or large-scale data infrastructure would be advantageous

An interest in leading projects

What you'll get in return

As well as a highly competitive salary and package, plus bonus, you'll have the chance to make a significant real-world impact in the field of genomics.You'll be working with highly capable and collaborative scientists and have access to significant compute resource.

There's also flexibility around working styles and patterns and good opportunities for career progression and development.

What you need to do now

If you're interested in this role, click 'apply now' to forward an up-to-date copy of your CV, or call us now.

If this job isn't quite right for you but you are looking for a new position, please contact us for a confidential discussion on your career.

Keywords: Computational, Genomics, Scientist, Research, Algorithm, Development, ML, Machine, Learning, AI, Bioinformatics, Statistics, Python, Maths, Sequencing, Modelling, Analytics, Computational, Software, Data, Biology, Quantitative, Programming, Cloud, Innovation, Biotechnology, Research, NGS, Pathogen, Bacteria, Virus, Disease, Oncology

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