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Junior Data Analyst
Premium Job From CABI
Recruiter: CABI
Listed on: 14th November
Location: Wallingford, Oxfordshire
Salary/Rate: £23,000 - £26,000
Salary Notes: Competitive
Type: Contract
Start Date: ASAP
Reference: TJJDA18
Contact Name: Cabi
CABI is an international not-for-profit organization that improves people’s lives by providing information and applying scientific expertise to solve problems in agriculture and the environment. CABI is an intergovernmental organization that can trace its origins back to 1910. Our 48 member countries guide and influence our core areas of work, which include International Development and Publishing.

We have over 500 staff based in 16 countries. We have offices in Brazil, China, Ghana, India, Kenya, Malaysia, Pakistan, Switzerland, Trinidad & Tobago, the UK, the USA and Zambia.

International Development projects and research
Through knowledge sharing and science, CABI helps address issues of global concern such as improving global food security and safeguarding the environment. We do this by helping farmers grow more and lose less of what they produce, combating threats to agriculture and the environment from pests and diseases, protecting biodiversity from invasive species, and improving access to agricultural and environmental scientific knowledge.

CABI’s Knowledge Business
CABI produces key scientific publications, including CAB Abstracts - the world-leading abstracting and indexing database covering applied life sciences. We also publish multimedia compendia, books, eBooks and full text electronic resources aiming to further science and its application to real life. CABI invests its publishing surpluses directly into development projects, helping to improve livelihoods worldwide.

Purpose of the role:
The Plantwise programme has collected a large amount of data on plant pest and disease occurence from its operations, most notably its plant clinics. Greater emphasis is now being put on its use and reuse in a range of contexts, within CABI, in national systems and among partners. Data assets of Plantwise are a strategic programmatic concern and there is a need to improve the quality of insights gained from them. We are looking for an early-career scientist with biology or agriculture background and strong data skills, who can help us collate, validate, manipulate, analyse, store and link large and complex datasets and provide quality assurance to these processes. Some experience in both geospatial data and analytics will be required especially in relation to trade, weather and demographic data.

While the focus of the role is primarily on Plantiwse, the Knowledge Management & Data Team have a number of data-focused projects, and there may be opportunities to get involved in other areas.

Key accountabilities:
   •  Sourcing third party datasets that add value to Plantwise data, inventorising assets and processing permissions, assessing ownership rights to reuse data
   •  Processing, checking, refining and analysing big and complex datasets from Plantwise and its partners
   •  Support to Geospatial Data Analyst and others on data science initiatives within Plantwise, including:
   •  Applying machine learning to clean plant clinic data and extracting insights from natural language text
   •  Applying statistical methods to anonymize and aggregate plant clinic data in preparation for publishing
   •  Spatial modelling and GIS analysis
   •  Using Plantwise clinic data in data modelling
   •  Support to development of data management proceedures and tools to facilitate collaboration within CABI and broader research communities.
   •  Support Plantwise scientists and development professionals to produce high quality, reusable, FAIR, and where necessary open data from their activities.
   •  Collaboration in writing scientific publications including emphasis on data publications.


We are seeking a curious and enthusiastic individual with strong data skills to provide support for advancing data science work within Plantwise.

Knowledge & Skills

   •  Data analysis, vizualisation, and reporting
   •  Proficiency in at least one statistical programming language, such as R or Python
   •  Good GIS skills: geospatial analyses and cartography
   •  Knowledge of biological, agricultural, environmental datasets and relevant statistical methods in these fields
   •  Knowledge of relational databases and skills in administering, transforming and manipulating large datasets
   •  Knowledge of good data management practices, scientific data standards and FAIR data principles
   •  Proficiency in Microsoft Office Suite of packages.
   •  Fluent in English
   •  Excellent written communication skills.

   •  Knowledge of data preprocessing for machine learning

Education & Qualifications

   •  A good degree in a scientific or numerate subject

   •  A higher degree or equivalent in a scientific or numerate subject

   •  Project and operational experience within a complex and information rich environment, working with large and complex datasets, managing different data types and standards across a range of subject domains
   •  Experience in GIS and its application in scientific research and development projects
   •  Experience of working in multi-disciplinary teams and delivering outputs for technical and non-technical audiences

Personal Characteristics
   •  Investigative and analytical with a willingness to embrace new approaches and technology
   •  Accurate; attention to detail and quality
   •  A team player with good interpersonal and communication skills;
   •  Self-motivated, able to work independently;
   •  Capacity to handle a demanding workload;
   •  Willingness to travel both in the UK and worldwide;
   •  Good personal organisational and problem solving skills;

(12 months FTA, option for extension)

CABI is an equal opportunities employer and welcomes applications from candidates irrespective of age, gender, race, colour, nationality, ethnic or national origin, disability, religion, sexual orientation or marital status. No applicant will be disadvantaged by conditions which cannot be shown to be justified and selection will be based on merit.

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