Data Scientist - Antimicrobial Resistance, UK
Wellcome Sanger Institute is seeking an expert Data Scientist to work on mining and modelling pathogen phenotypic, genomic and epidemiological data for the identification and surveillance of risk patterns. You will work with a team of software engineers, genomic epidemiologists and microbial genomic researchers based at The Centre for Genomic Pathogen Surveillance (CGPS) and lead the utility and application of ML and data analytic methods across our project workspaces.
The role is part of a NIH grant on ‘Big Data for knowledge’ and is a collaboration between the Centre for Genomic Pathogen Surveillance at the Wellcome Sanger Institute and the WHO Collaborating Centre for surveillance of antimicrobial resistance, Boston, USA.
The partner on this project, the WHO Collaborating Centre for Surveillance of Antimicrobial Resistance, has been responsible for the development of a widely used software called WHONET (120 countries, over 10,000 labs), which allows laboratories to collate microbiological and phenotypic data on isolates (samples from patients), including whether they are resistant or sensitive to sets of antimicrobials.
The Data Scientist’s role will be to help develop analytic frameworks for data collated from a large number of WHONET instances (and similar platforms) to develop intelligent alerting for flagging the emergence of risk and the subsequent intelligent use of Whole Genome Sequencing (WGS) (eg for risk flagging, diagnostics and inference of transmission).
- The expertise in one or more of data mining, statistical analysis, machine learning and modelling and a thirst for utilising your expertise to provide methods for use in real world applications.
- The post will involve substantial liaison with local and international agencies and partners in Low and Middle Income Countries (LMICs) focussed on the generation of and intelligent use of surveillance data for the control of AMR.
• Advanced degree in Computing, Mathematics, Statistics or similar numerical discipline
• Strong programming skills (including statistical modelling eg Python or R).
• Experience using machine learning algorithms.
• Demonstrable quantitative, qualitative research and analytics experience.
• The ability to come up with solutions to loosely defined analytic problems by using pattern detection over potentially large datasets.
• Desire and ability to work with other quantitative and information disciplines to explore where Data Science and ML can complement existing work and help drive surveillance of AMR in the future.
• A high level of interpersonal skills to be able to elicit complex requirements from, and convey complex requirements to, groups with differing technical backgrounds.
• Ability to prioritise tasks, organise work effectively and crucially, work as part of a team.
• Ability to present and give positive input and discussion at meetings.