Location: | Oxfordshire, Wallingford, Work from home |
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Salary: | £27,965 to £37,836 |
Hours: | Full Time |
Contract Type: | Permanent |
Placed On: | 17th June 2022 |
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Closes: | 17th July 2022 |
THE ROLE
CABI is establishing itself as an expert in facilitating the development of tools and products which utilise a variety of data sources and modelling approaches, combined with an understanding of Integrated Pest Management (IPM), Good Agricultural Practice (GAP) and human-centred design (HCD). These tools and products enable stakeholders to make informed decisions and access information relating to crop health. As an organisation, CABI produces and collates large amounts of ecological, environmental, socio-economic and earth observation (EO) data through its science and major programmes; including Global Burden of Crop Loss (GBCL), PRISE, Plantwise+, Action on Invasives and CABI Compendia.
We are looking for a data analyst to work across CABI digital development programmes, with a focus initially on Global Burden of Crop Loss (GBCL). GBCL aims to generate comprehensive, authoritative evidence on the magnitude of crop losses worldwide, disaggregated by crop, biotic and abiotic causes. GBCL is modelled after the Global Burden of Disease initiative in human health, which has transformed health policy, research, and funding over the last 25 years through better use of data.
WHAT YOU’LL DO
In your work in the GBCL programme:
CANDIDATE PROFILE
CABI is looking for a curious and enthusiastic individual with strong quantitative skills to provide support for advancing data science work within our programmes in digital development. This post will support a growing portfolio of data modelling and analytics projects within CABI’s digital development theme.
This role is suitable for a data analyst with a background in agriculture/biosciences and with spatial data analysis skills, to support the GBCL initiative in collating, validating, and analysing large and complex datasets in a reproducible manner. The candidate will work in global, multidisciplinary teams on the implementation of the programme modelling approaches to generate estimates of crop loss and economic burden. Knowledge and experience of at least one statistical programming language is required. Skills in geospatial analysis, applying quantitative methods addressing ecological/agricultural problems, and understanding of the principles of pest biology are of key importance.
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