Data Scientist (P3), Early Warning and Effective Response System Project

Job Posting Organization:
The job is posted by the United Nations High Commissioner for Refugees (UNHCR), which was established in 195

• The organization is dedicated to protecting the rights and well-being of refugees and displaced persons worldwide. UNHCR operates in over 130 countries and employs more than 18,000 staff members. Its mission is to ensure that everyone has the right to seek asylum and find safe refuge in another state, and to return home voluntarily, integrate locally, or resettle in a third country.

Job Overview:
The scientist" style="border-bottom: 1px dotted #007bff !important;">Data Scientist will play a crucial role in the Early Warning and Effective Response System Project, which aims to enhance UNHCR's ability to forecast humanitarian emergencies and the impacts of displacement. The position involves working closely with UNHCR colleagues, the Luxembourg Institute of Science and Technology (LIST), and external partners to develop predictive models and data-driven strategies for humanitarian action. The Data Scientist will be responsible for conducting research, developing machine learning models, and ensuring the effective collection and analysis of data to inform decision-making processes. This role requires a strong background in machine learning, data analysis, and collaboration with diverse stakeholders to achieve project objectives.

Duties and Responsibilities:
The Data Scientist will have a wide range of responsibilities, including: conducting research to analyze existing early warning models; developing machine learning models to process multi-source data; designing a multivariate time series-based early warning system; collecting and organizing data in compliance with UNHCR’s Data Management Guidelines; evaluating and debugging models; incorporating user feedback into the forecasting system; and maintaining documentation of processes and findings for publication. The role also involves identifying pilot countries for testing models, assessing displacement risks, and developing strategies for data collection and analysis.

Required Qualifications:
Candidates must possess a degree in a relevant field such as machine learning, artificial intelligence, applied mathematics, or computer sciences. They should have excellent programming skills, experience with machine learning methods, and a solid understanding of statistical modeling and optimization techniques. Strong communication skills, the ability to work independently and collaboratively, and fluency in English are mandatory. Desirable qualifications include a Master’s or PhD in a related field, knowledge of forced population displacement, and experience with data visualization technologies and cloud computing services.

Educational Background:
A Bachelor’s degree in a relevant field is required, with a preference for candidates holding a Master’s degree or PhD. The educational background should be related to machine learning, artificial intelligence, human interaction systems, applied mathematics, computer engineering, telecommunications engineering, or similar disciplines.

Experience:
Candidates should have a minimum of 6 years of relevant work experience with a Bachelor’s degree, 5 years with a Master’s degree, or 4 years with a PhD. Experience should include work in machine learning, data analysis, and the development of forecasting systems, as well as familiarity with data mining and scientific research presentation.

Languages:
Fluency in English is mandatory. Knowledge of additional languages is considered an asset but is not required for this position.

Additional Notes:
This position is based in Luxembourg and is classified as a senior-level professional role. The target start date for the position is January 19, 202

• The role may involve travel to pilot countries for short periods, and it is important to note that the position does not require functional clearance. The contract duration and specific compensation details are not provided in the job description.