THE UNIVERSITY OF MANCHESTER PARTICULARS OF APPOINTMENT FACULTY OF MEDICAL & HUMAN SCIENCES INSTITUTE OF POPULATION HEALTH

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1 THE UNIVERSITY OF MANCHESTER PARTICULARS OF APPOINTMENT FACULTY OF MEDICAL & HUMAN SCIENCES INSTITUTE OF POPULATION HEALTH CENTRE FOR HEALTH INFORMATICS HEALTH DATA SCIENTIST Vacancy ref: M&HS Salary: Hours: 30,738-37,768 per annum 1.0 FTE Duration: Fixed term, until 31/01/2018 Location: Oxford Road, Manchester Responsible to: Niels Peek Enquiries about the vacancy, shortlisting and interviews: Niels Peek, Reader in Health Informatics Tel: BACKGROUND The University of Manchester The University of Manchester is Britain s largest and most popular university, with a distinguished history of academic achievement and an ambitious agenda for the future. The University, with income in excess of 780 million, has four Nobel Prize winners amongst its current academic staff, and has embarked on an exciting and bold course which aims to make us one of the top 25 universities in the world, as set out in the University s strategic vision for 2020: Strategic Vision The University of Manchester was established in 1824, bringing together The Victoria University of Manchester and UMIST to form The University of Manchester. It has an excellent track record in research, as demonstrated by a research power exceeded only by Oxbridge in the RAE 2008 and specifically by the award of two Nobel prizes in physics in The University s research strategy can be found via the following link: UoM Research Strategy. The academic structure of The University of Manchester is made up of Faculties and Schools. There are four Faculties and further information about each Faculty and its related Schools can be found at UoM Faculties and Schools.

2 The Faculty of Medical and Human Sciences (FMHS) The Faculty of Medical and Human Sciences is a leading international centre for research and education in medicine and a spectrum of health-related professions including nursing, midwifery, social work, pharmacy, dentistry, psychology, audiology and speech and language therapy. A major review in 2011/12 led to a new strategy and structure intended to enhance our research and teaching performance. A key component of the new approach is the creation of a matrix structure linking five Faculty Schools with six Faculty Institutes. The objective is to ensure that the Faculty achieves a major international academic profile in each priority area within a period of five years. The new strategy will focus our research effort in six priority areas: Human Development including Women s and Children s Health Cardiovascular Sciences Cancer Science Inflammation and Repair Brain, Behaviour and Mental Health Population and Health Sciences Details of the structure can be found at: FMHS Structure. FMHS has a total income of 210 million, around 6,500 undergraduate students and 2,500 post graduate students. The Faculty has strong relationships with outstanding NHS partners which are critical in achieving our mission. Manchester Academic Health Science Centre (MAHSC) The University, and in particular the Faculty of Medical and Human Sciences, is a key member of the Manchester Academic Health Science Centre (MAHSC). Formed in 2008, MAHSC is a Federation of Equal Partners enabled by a Company Limited by Guarantee. The partners involved in the MAHSC are: The University of Manchester, Central Manchester University Hospitals NHS Foundation Trust, Manchester Mental Health and Social Care Trust, Salford Primary Care Trust (NHS Salford), Salford Royal NHS Foundation Trust, The Christie NHS Foundation Trust and University Hospital of South Manchester NHS Foundation Trust. MAHSC is one of only five Department of Health designated AHSCs in the UK. The designation is a mark of excellence across research, innovation, education and patient service, and recognition of the potential to excel in translational medicine. MAHSC s vision is to be a leading global centre for the delivery of innovative applied health research and education into healthcare. As with other AHSCs, MAHSC has a dual role: to act as a beacon of international excellence for UK plc and to provide leadership and early adoption for our local health system. This will be delivered via a tripartite approach encompassing: research and innovation; education and training and clinical service. For further information, please refer to the MAHSC website: Institute of Population Health Population Health is home to approximately 300 staff across 6 Centres: Biostatistics, Epidemiology, Health Economics, Health Informatics, Imaging Sciences and Primary Care. Staff

3 are based largely on the University campus, but also at the Wolfson Molecular Imaging Centre (Withington) and Salford Royal NHS Foundation Trust (formerly Hope Hospital). The Institute s goal is to enable The University of Manchester to reach its full potential as an internationally esteemed centre for research and education in population health and health sciences. This knowledge can then be translated into improved health and wellbeing for people locally, nationally and internationally. The Institute s areas of international expertise include: methodological research including advanced methodology in biostatistics, health economics, informatics and imaging; health policy and practice focused broadly on: analysing government policy as it affects health and health care; exploring the impact of changing policy; describing, explaining and evaluating variations in health care organisation and delivery; and developing and evaluating strategies to improve care, ranging from policy interventions to trials of complex interventions to improving patient outcomes; and population health which investigates areas such as the factors determining disease risk, variation in treatment response, clinical progression and outcome. It also applies epidemiological approaches to the analysis of biological samples and clinical data generated through the University s bio-banking facilities (CIGMR) and Northwest e-health (NWEH). Similar methodologies are deployed to describe, explain and evaluate variations in care provision, exploiting the unique database resources generated by NDEC (drug misuse), TARN (trauma) and COEH (occupational health). The Centre for Health Informatics The Centre for Health Informatics, led by Professor Iain Buchan, hosts the MRC Health eresearch Centre (HeRC). HeRC conducts world-leading research in health informatics and forms a centre of excellence in digital health innovation for North England. The Centre has a national role in driving advanced methodological research to harness health data; and to build capacity in health informatics. HeRC is a founding partner of the national Farr Institute of Health Informatics which has been established to create a network of safe havens for deep analysis of electronic health records at UK-scale. The Farr Institute also provides an environment for industry and academia to collaborate over UK health data, methods and expertise in in a timely, cost effective and citizen-engaged way, benefiting the UK economy. The Centre runs a portfolio of research with over 20 projects/programmes with a value in excess of 60M. The Centre is actively involved in delivering major initiatives including: NIHR programmes, including one of the two National Patient Safety Research Centres; large European programmes such as EHR4CR, EMIF; international partnerships such as a new exchange scheme in biomedical informatics with Harvard University; and the Health North programme to build Connected Health Cities across North England. OVERALL PURPOSE We are looking for a health data scientist with advanced knowledge and skills in statistical and machine learning methods for analysis of large health datasets. You will contribute to our core research programme. Specifically you will have central role in the MRC-supported Health eresearch Centre (HeRC). You will be based at the University of Manchester in the Centre for Health Informatics (CHI) and will work alongside the Centres internationally renowned academic staff.

4 JOB DESCRIPTION You will drive innovation at the interface between biostatistics, machine learning, and software engineering. Working with clinical, IT and healthcare data experts the health data scientist will be to drive the modelling of health data using state-of-the-art methods from biostatistics and machine learning. The available data will include anonymised, linked extracts of primary and secondary care records. Using these data, flows of patients can be tracked across a health system. Analyses can then uncover deviations from agreed pathways of care and potential missed opportunities in patient management; for example, issues around safe prescribing of medication. Such data can also be used for predictive modelling, and there are opportunities to build on existing statistical methodology in order to embrace and exploit the complexity of the real-world data to improve outcome prediction. KEY RESPONSIBILITIES Key accountabilities Expert statistical analyses to support HeRC and CHI research outputs. Methodological and applied scientific excellence in support of HeRC and CHI. Operational responsibilities and tasks Provide expert data analyses with a range of biomedical and healthcare problems and datasets. Contribute to the development and management of a world-renowned research capability focused on extracting knowledge from large repositories of anonymised healthcare data. Work with HeRC and CHI software engineers to embed frequently required analytical processes into the software and workflows. Prepare statistical training materials with the learning and development team. Work with clinical researchers affiliated to HeRC, and CHI to ensure scientific synergy, and good scientific practice. Contribute to the development and management of a scientific network to support and enhance HeRC and CHI objectives. Work with colleagues to promote HeRC research, including applying for new research projects and grants. Contribute to scientific goal-setting, and the development of scientific excellence. Ensure effective communication with the CHI and HeRC senior management teams. Contribute to communications with CHI, HeRC staff & affiliates, key stakeholders, opinion-leaders, policy-makers, and public and patient interest groups, as required. All posts are subject to a Criminal Records Bureau check.

5 PERSON SPECIFICATION Essential Previous relevant experience in academia or industry encompassing a strong mathematical element and significant statistical knowledge - preferably with experience in the analysis of complex healthcare problems and datasets. A PhD in Statistics, Machine Learning, or related fields; OR a higher degree in Statistics, Machine Learning, or related fields, with significant and relevant research/industry experience. A proven ability to work effectively within a team to achieve timely and valued objectives. Significant statistical modelling and programming experience. Demonstrable experience of balancing challenging workloads. Experience of publishing material in peer-reviewed journals. Desirable Broad-based experience in: Statistical and machine learning analyses of health, biological or social datasets. Epidemiological models and complex causal inference. Multi-level regression models. Training others in statistical analysis and/or machine learning. Good presentation skills. Ability to explain complex statistical issues to a non-statistical audience. Strong publication record.

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