Machine Learning Expert: Impact-Based Climate Forecasting Eastern Africa

Role and objective

The Machine Learning Expert will be responsible for developing a comprehensive machine learning impact based forecasting (IBF) system to support decision-making and enhance resilience in Eastern Africa. The IBF system will leverage socio-economic, meteorological, and impact data to provide forecasts that emphasize the consequences of weather/climate events. This system will be designed to provide actionable insights, improve resource allocation, support proactive financial planning, and enhance preparedness across key sectors.

Main tasks and responsibilities

Data Collection & Analysis:

• Conduct an assessment to identify gaps and limitations in current datasets related to impact-based forecasting.
• Collect and pre-process datasets, including historical weather data, agricultural yield data, flood records, and socio-economic indicators.

Model Development:

• Develop and implement machine learning models for predicting potential impacts on agriculture, water resources, and vulnerable populations using climate and socio-economic data.
• Test and refine the models to account for varying impacts across different geographical regions.

System Integration & Automation:

• Integrate the developed machine learning models into an automated system for regular data updates and real-time impact forecasting.
• Ensure seamless connectivity between various databases and forecasting tools.

Validation and Prototype Development:

• Validate model performance using historical weather events and impact records.
• Develop a prototype system for yield forecasting over key agricultural zones in Kenya (or other pilot regions as per the project plan).

Capacity Building & Training:

• Organise workshops and training sessions to enhance stakeholders’ understanding of the system and its outcomes.
• Prepare technical documentation and guidelines for the system’s usage.

Qualifications

• Advanced degree in Computer Science, Data Science, Statistics, Geo-Informatics, Meteorology or Climate Sciences or equivalent
• At least five years of experience, two of which were in developing Machine Learning methods to solve specific problems, with particular interest towards scientific applications
• Experience developing, debugging and applying models using modern deep learning frameworks
• Proficiency in scripting and programming languages preferably Python and/or R programmes; experience analyzing big data
• Experience with ML systems using frameworks such as Scikit-learn and Tensorflow
• Good understanding of Machine Learning concepts and methods when to apply them and how to effectively implement them using the available machine learning packages is key
• Familiarity with Git, Docker, AWS or equivalent
• Experience and understanding of statistical and geo-statistical techniques and how to apply them in various contexts including climate applications.
• Ability to describe findings and the way techniques work to audiences, both technical and non-technical and visualisation of the results using various tools.
• Experience in integrating and visualising products and outputs on web-based platforms. An understanding of various web development techniques to make products available on frontend systems from backend procedures is desirable.
• Experience/interest in climate data, climate science and disaster risk