Job Openings at Norwegian Refugee Council

What you will do       

• Ensure compliance and adherence to NRC policies, procedures, tools, minimum standards, personal data protection rules as it relates to international contracting. 
• Adhere to service level agreements with all relevant NRC stakeholders. 
• Identify risks and report any deviation related to medical vetting and contractual procedures. 
• Act as an Advisor for all compensation and benefits related queries; contractual queries; employment documentation queries, medical clearance queries  
• Respond to ad hoc requests and manage all communication sent to the Recruitment Centre. 

  What you will bring      

• Work closely with recruiting teams and relevant HR colleagues to obtain timely information for the preparation of international job offers and contract extensions. 
• Initiate, monitor and follow-up on pre-employment processes in accordance with NRC requirements. 
• Create MedFit profiles and initiate MedFit requests for new staff and their dependents where relevant. 
• Input bio-data information, project code, insurance and payroll details for each employee prior to issuance of the contract, to ensure accuracy in pension processing, accuracy in cost allocation and registration in the insurance scheme. 
• Provide payroll with information regarding employees’ contract, allowance and location changes and other relevant documents to ensure correctness in payroll processing. 

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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