Data Scientist Genomic Epidemiology - Pathogen

Led by a world-class faculty of scientists, technologists, policy makers, economists and entrepreneurs, the Ellison Institute of Technology aims to develop and deploy commercially sustainable solutions to solve some of humanity’s most enduring challenges. Our work is guided by four Humane Endeavours:
Health, Medical Science & Generative Biology, Food Security & Sustainable Agriculture, Climate Change & Managing Atmospheric CO2 and Artificial Intelligence & Robotics.

Set for completion in 2027, the EIT Campus in Littlemore will include more than 300,000 sq ft of research laboratories, educational and gathering spaces. Fuelled by growing ambition and the strength of Oxford’s science ecosystem, EIT is now expanding its footprint to a 2 million sq ft Campus across the western part of The Oxford Science Park. Designed by Foster + Partners led by Lord Norman Foster, this will become a transformative workplace for up to 7,000 people, with autonomous laboratories, purpose-built laboratories including a plant sciences building and dynamic spaces to spark interdisciplinary collaboration.

The Pathogen Mission highlights EIT’s transformative approach, using Whole Genome Sequencing (WGS) and Oracle’s cloud technology to create a global pathogen metagenomics system. This initiative aims to improve diagnostics, provide early epidemic warnings, and guide treatments by profiling antimicrobial resistance. The goal is to deliver certified diagnostic tools for widespread use in labs, hospitals, and public health.

EIT Oxford fosters a culture of collaboration, innovation, and resilience, valuing diverse expertise to drive sustainable solutions to humanity’s enduring challenges.

We are seeking a
Data Scientist in Genomic Epidemiology
to support the scientific development and implementation of EIT Oxford's Pathogen Programme. Reporting to Head of Population Data Science, the role involves collaborating with internal teams and external partners to assess, develop methods for, and implement at scale, computational and statistical methods for analysing the genomic, phenotypic and epidemiological characteristics of a variety of pathogens in order to inform public health applications ranging from AMR monitoring to outbreak detection and vaccine deployment.

The postholder will carry out research, develop and evaluate high quality software for integration into the EIT Pathogen platform, present findings in peer-reviewed publications and at international forums, contribute to the design and development of large-scale data resources, and explore innovative uses of data to evaluate and improve public health policy and interventions. Ideal candidates will have expertise in high throughput WGS applications within infectious disease epidemiology and monitoring, a strong academic background, excellent skills in research software development, and experience of working with global partners to develop, evaluate and embed new capabilities for data-driven public health.

• In partnership with the EIT Data and AI team, establish and optimise best practice for managing population-scale genomic and phenotypic data, with appropriate metadata, for downstream analysis.
• In partnership with the Product and Medical Teams, define use cases for data products and services that generate insights from population-level data on pathogen genotypic and phenotypic diversity for public health applications.
• Carry out research and development to establish best-in-class analytics for population-scale data science to characterise, analyse and evaluate the potential impact of interventions for applications such as AMR monitoring, outbreak detection, vaccine deployment, community intervention and clinical trials.
• Deliver high quality software to perform such applications, which can be integrated into the EIT Pathogena Platform by the Technology Team.
• Present work at international meetings and publish in peer-reviewed journals
• Work with external partners, where appropriate, to enable knowledge transfer and to support establishment of best practices for genomic and related data analysis using the products and services developed by EIT.