NORCAP’s mission is to improve aid to protect and empower people affected by crises and climate change. With expertise in the humanitarian, development and peacebuilding sectors, we collaborate with local, national and international partners on finding solutions that meet the needs of people at risk.
We do this by:
Supporting humanitarian, development and peacebuilding initiatives that protect and empower people. Improving impact at the global and local level through joint projects with partners and stakeholders Providing expertise and developing capacity that enable partners to meet the needs of people in fragile situations and crises. Strengthening the global system that we are part of through support for leadership, coordination and policy development. Building bridges between the humanitarian, development and peacebuilding sectors NORCAP is part of the Norwegian Refugee Council.
We are recruiting a Machine Learning Expert with experience in impact-based forecasting for a one-year assignment with possible extension to be based at the IGAD Climate Prediction & Applications Center (ICPAC) in Nairobi, Kenya.
Background
Eastern Africa is highly vulnerable to a range of natural disasters. Climate shocks such as extreme floods, prolonged droughts, and pest outbreaks have shown an increasing trend, expected to worsen with global warming, thereby threatening the resilience of communities.
Impact-Based Forecasting (IBF) is crucial for mitigating these risks by shifting the focus from traditional weather and climate forecasting to predicting the potential impacts of these events. IBF aims to provide actionable insights to stakeholders for early interventions and risk mitigation measures, especially in sectors like agriculture, which is a significant contributor to the region’s GDP.
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
What we offer
Rewarding work for a renowned global organisation Access to a network of humanitarian, peace, and development professionals Join NORCAP’s team of experts and be considered for future assignments elsewhere A dedicated Staff Care UnitHow to apply
When submitting your application, kindly register in English your full employment history and education. Please include your full name as written in your passport. Uploaded CVs alone will not be reviewed. NORCAP values diversity, equity and inclusion. We welcome applications from all qualified candidates, regardless of race, sex, sexual orientation, national origin, religion or disability. Both national and international candidates are encouraged to apply. NORCAP reserves the right to conduct a full background check on shortlisted candidates. Approved Health Certificate will be required prior to contract commencement and assignment. Feedback will be given to all applicants within four weeks after the closing of this advertisement. Apply before 8th December 2024, 11.59PM CET.