Research Fellow in Health Data Science
Posted 5 days ago by Totaljobs
£51,872 Per year
Outside
Onsite
London; City of London; East London; Central London; South East London; West London; Canary Wharf; South West London; North London; Greenwich; Stratford
Summary: The London School of Hygiene & Tropical Medicine is seeking a Research Fellow in Health Data Science, focusing on machine learning for the NeoShield program, which addresses neonatal sepsis and antimicrobial resistance. The role involves leading the development and evaluation of machine-learning systems for clinical decision support and outbreak detection. Candidates should possess a relevant postgraduate degree and experience in machine learning with real-world datasets. This full-time, fixed-term position is funded by the Wellcome Trust and based in London.
Key Responsibilities:
- Lead the design, development, deployment, and evaluation of machine-learning systems for NeoShield.
- Develop a Clinical Decision Support Tool for neonatal sepsis diagnosis.
- Create a real-time ward-level outbreak detection system using time-series analysis and anomaly detection methods.
- Integrate clinical, microbiological, and data science approaches to generate evidence for infection management.
Key Skills:
- Postgraduate degree, ideally a doctoral degree, in machine learning, data science, computer science, biostatistics, epidemiology, or a related quantitative field.
- Applied experience in machine learning.
- Extensive hands-on experience in model development, testing, validation, and deployment.
- Experience with real-world datasets in operational or research settings.
Salary (Rate): £51,872 yearly
City: London
Country: United Kingdom
Working Arrangements: on-site
IR35 Status: outside IR35
Seniority Level: Mid-Level
Industry: IT
Department Department of Infectious Disease Epidemiology and International Health Salary £45,728 to £51,872 per annum pro rata inclusive Closing Date Tuesday 21 April 2026 Reference EPH-EPIH-2026-01-R
The London School of Hygiene & Tropical Medicine (LSHTM) is one of the world’s leading public health universities. Our mission is to improve health and health equity in the UK and worldwide; working in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice.
The Department of Infectious Disease Epidemiology & International Health is seeking to appoint a Research Fellow in Health Data Science (with a focus on machine learning) to NeoShield, a multi-country implementation research programme focused on neonatal sepsis and antimicrobial resistance, led by LSHTM in collaboration with the Malawi-Liverpool-Wellcome Trust and the Zambia National Public Health Institute.
NeoShield integrates clinical, microbiological, and data science approaches to generate evidence and tools for safer, more targeted infection management in hospitalised newborns. The post-holder will lead the design, development, deployment and evaluation of NeoShield's two core machine-learning systems: a Clinical Decision Support Tool for neonatal sepsis diagnosis (predicting likelihood of infection and informing antibiotic decision-making), and a real-time ward-level outbreak detection system (using time-series analysis and anomaly detection methods).
The post-holder must have a postgraduate degree, ideally a doctoral degree, in a relevant discipline (e.g., machine learning, data science, computer science, biostatistics, epidemiology, or another quantitative field), and applied experience in machine learning, with extensive hands-on experience of model development, testing, validation and deployment using real-world datasets in operational or research settings. Further particulars are included in the.
The post is full-time 35 hours per week, 1.0 FTE and fixed-term for 24 months with potential for extension subject to funding. The post is funded by Wellcome Trust and available immediately.
The salary will be on the LSHTM salary scale, Grade 6 in the range £45,728-£51,872 per annum pro-rata (inclusive of London weighting). The post will be subject to the LSHTM terms and conditions of service. Annual leave entitlement is 30 working days per year, pro-rata for part-time staff. In addition to this there are discretionary “Wellbeing Days”. Membership of the Pension Scheme is available. The post is based in London at LSHTM.
Applications should be made on-line . Online applications will be accepted by the automated system until 10pm of the closing date. Any queries regarding the application process may be addressed to .
The supporting statement section should set out how your qualifications, experience and training meet each of the selection criteria. Please provide one or more paragraphs addressing each criterion. The supporting statement is an essential part of the selection process and thus a failure to provide this information will mean that the application will not be considered. An answer to any of the criteria such as "Please see attached CV" will not be considered acceptable.
Please note that if you are shortlisted and are unable to attend on the interview date it may not be possible to offer you an alternative date.
The London School of Hygiene & Tropical Medicine is committed to being an equal opportunities employer. We believe that when people feel respected and included, they can be more creative, successful, and happier at work. While we have more work to do, we are committed to building an inclusive workplace, a community that everyone feels a part of, which is safe, respectful, supportive and enables all to reach their full potential.