Job description

We are searching for a talented data scientist to assist in developing statistical models, particularly machine learning models, to improve healthcare outcomes for people with epilepsy in low- to middle-income countries. The Epilepsy Pathway Innovation in Africa (EPInA project is a large NIHR multinational study that aims to improve the lives of people with epilepsy within Africa and beyond. Leveraging from this, the Oxford Martin Programme on Global Epilepsy aims to develop mHealth technologies to help in the diagnosis, management and education of people with epilepsy and their local communities. We will be working in Africa (Kenya, South Africa, Zimbabwe) as well as in Brazil and India with the possibility to expand to other regions.
Using our routinely updated and expanding datasets from these regions, we are developing mathematical models that will be implemented into mHealth-based clinical tools to assist healthcare workers in improving the diagnosis and treatment of people with epilepsy. Our work is grounded in region- and culture-specific research and encompasses collaborations from a broad range of disciplines, including clinical medicine, basic science, engineering, software development, history and anthropology. This role is likely to include travel to host regions (COVID-dependent) enabling working within local communities to develop, implement, refine and validate these models.
You will have a degree in a relevant subject and a post-graduate degree in Data Science, Mathematics, Statistics, Computing, or a related subject, and you will also have demonstrable experience, ability and practical success in data science, including independent ability to develop machine learning models. It is also essential to have demonstrable ability to organise and prioritise work efficiently whilst delivering results to the required standard and to an agreed schedule. Previous experience in developing machine learning models for clinical applications is a desirable.
The post is full time for a fixed term until 31st October 2023 in the first instance.
Only applications received before 12.00 midday on 09.08.2022 will be considered.
Interviews will be held as soon as possible thereafter.

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