We are seeking a skilled data scientist and science communicator to support a new national research network around healthy ageing. The successful candidate will use machine learning techniques to analyse large data sets held within the network, with the aim of producing novel findings within the field of metabolic ageing. The role will involve engaging with academics across Queen Mary and externally, including writing reports, newsletters, and presenting at events.
You will have a degree or relevant professional experience in data science or another related quantitative/scientific discipline. You will have experience using machine learning techniques and will be confident in communicating your findings to a range of specialist and non-specialist audiences.
The network is funded by the BBSRC-MRC’s Ageing across the life course interdisciplinary research network call. This national network of stakeholders is led by Profs Chan and Henson and aims to tackle CELLular metabolism Over a life-course in socioeconomically disadvantaged populations (CELLO).
We offer competitive salaries, access to a generous pension scheme, 30 days’ leave per annum (pro‑rata for part‑time/fixed‑term), a season ticket loan scheme and access to a comprehensive range of personal and professional development opportunities. In addition, we offer a range of work‑life balance and family‑friendly, inclusive employment policies, flexible working arrangements, and campus facilities including an on‑site nursery at the Mile End campus.
The post is based at the Charterhouse Square Campus in London. It is full time (35 hours per week), fixed‑term appointment for 10 months, with an expected start date of May 2024. The starting salary will be Grade 5, £42,405 – £45,932 per annum, inclusive of London Allowance.
Queen Mary’s commitment to our diverse and inclusive community is embedded in our appointments processes. Reasonable adjustments will be made at each stage of the recruitment process for any candidate with a disability. We are open to considering applications from candidates wishing to work flexibly.