Kenya Red Cross is one of the many International Red Cross and Red Crescent Movement societies around the world. The Kenya organisation was established in 1965, The Kenya Red Cross supports and runs a number of projects whilst raising awareness to the Kenyan public about the current issues or problems which may affect them.
PURPOSE:
The data analyst is responsible for analysing data, combining different datasets and designing and delivering information products that are of use to the Red Cross. This person works closely together with the GIS expert and reports to the humanitarian data team lead.
Key Responsibilities
- Understand information needs by Kenya Red Cross decision makers and translate these into data analysis and information products.
- Develop data models for data intensive projects, applying standards where possible.
- Support Kenya Red Cross in using the products, maps, and visualizations provided by the team.
- Review available open – and closed – (secondary) data sources and determine if these can be used instead of collecting new data.
- Review and cleaning of data.
- Analysis of data, both small and big. Development of algorithms to combine and analyse these data.
- Support and guide students and volunteers that work in the team on data analysis.
Minimum Qualifications and Experience
- A University degree in econometrics or other data analysis related studies.
- Record of accomplishment showing the capacity to use data to improve processes, operations and decision-making.
- At least 3 years of professional experience in data analysis.
Key Competencies
- Demonstrable interest in humanitarian aid.
- Research skills to determine the effectiveness of the products and services provided by the team.
- Skilled in data analysis software / languages such as R and/or Python.
- Excellent command of English language (written and verbally).
- Work experience in an international development or humanitarian setting.
- In-depth experience in any of the following subject matters: data analysis, data visualization, machine learning, data literacy, GIS or programming.
- Committed to customer excellence.
- Flexibility and ability to work under pressure.
- Self-confidence.
- Strong listening and feedback skills
- Being able to communicate clearly and concisely (written and verbally).
- Being trustworthy.
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