Medical Statistician

Job title:

Medical Statistician

Company

University of Oxford

Job description

We are recruiting for a Medical StatisticianAbout the RoleJoin one of the country’s leading groups working in infectious diseasesThe Modernising Medical Microbiology (MMM) consortium at the University of Oxford is amongst the country’s foremost groups working in infectious diseases in the UK, aiming to improve diagnostics and fight antimicrobial resistance. We undertake large-scale epidemiological studies of electronic health records data and link this to data from pathogen sequencing, using a diverse range of sequencing technologies.We are looking to recruit an enthusiastic MSc or PhD graduate with a background in mathematics, statistics or medical statistics to join our group, who is interested in training “on-the-job” to build a successful career as a medical statistician. Depending on the candidate’s interest and skills, for applicants without a PhD, there would be an opportunity to register for a PhD in future, depending on performance and further funding.This role will include practical on-the-job training working within the friendly multi-disciplinary group made up of around 30 scientists with expertise in biostatistics, epidemiology, infectious diseases, microbiology, bioinformatics, and molecular biology, providing opportunities for skills and career development, both in terms of medical statistics and more broadly in terms of research careers. There will be opportunities to attend seminars at the University of Oxford to gain broader exposure to genetic research, and to complete other training depending on individual needs and interests.About YouThe successful candidate will have an excellent academic record, having achieved a first or upper second class degree in a numerate discipline (e.g. mathematics or mathematics and statistics, computer science, biology with a quantitative focus, or another quantitative subject), and having achieved or being on track to achieve a PhD or Masters in mathematics, statistics or medical statistics.They should have an enthusiastic hard-working approach, be keen to continue further formal study and to develop new skills. They should have good communication skills, and the ability to prioritise and work to deadlines. They should have good computer skills including MS Office and experience of one or more statistical programming languages.Candidates who do not fully meet the requirements for a Grade 7 post may be appointed at Grade 6: £34,046 – £40,855 per annumAbout the Nuffield Department of Clinical MedicineThe Nuffield Department of Clinical Medicine (NDM) is one of the largest departments of the University of Oxford and is part of the Medical Sciences Division, with responsibility for a significant part of the teaching of clinical students within the Medical School.What We OfferAs an employer, we genuinely care about our employees’ well-being and this is reflected in the range of benefits that we offer including:

• Flexible working

• An excellent contributory pension scheme

• 38 days’ annual leave (including bank holidays)

• A comprehensive range of childcare services

• Family leave schemes

• Cycle loan scheme

• Discounted bus travel and Season Ticket travel loans

The University also runs a large number of social groups and sports clubs for those looking for more than just a great place to work.Application ProcessThe post is full time and fixed term for 3 years in the first instance.Applications are to be made online. You will be required to upload a supporting statement and CV as part of your online application. Please include in your supporting statement why this is the right opportunity for you.Only applications received before 12.00 midday on Tuesday 4 February 2025 will be considered.Contact Person :HR TeamVacancy ID :177500Contact Phone :Closing Date & Time :04-Feb-2025 12:00Pay Scale :RESEARCH GRADE 7Contact Email :exp.med@ndm.ox.ac.ukSalary (£) :£38,674 – £46,913 p.a.

Expected salary

£38674 – 46913 per year

Location

Oxford

Job date

Thu, 09 Jan 2025 23:07:35 GMT

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