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Data scientist, p3

Genf
United Nations
Data Scientist
Inserat online seit: 14 Januar
Beschreibung

Job Details

Job Network: Public Information and Conference Management

Job Family: Data Analytics and Management

Category and Level: Professional and Higher Categories, P-3

Duty Station: GENEVA

Department/Office: United Nations Office for Disaster Risk Reduction

Job Opening ID: 269348

Deadline: Feb 5, 2026


About UNDRR

Created in December 1999, the United Nations Office for Disaster Risk Reduction (UNDRR) is the designated focal point in the United Nations system for the coordination of efforts to reduce disasters and to ensure synergies among the disaster reduction activities of the United Nations and regional organizations and activities in both developed and less developed countries. Led by the United Nations Special Representative of the Secretary-General for Disaster Risk Reduction (SRSG), UNDRR has over 160 staff located in its headquarters in Geneva, Switzerland, and in regional offices. Specifically, UNDRR guides, monitors, analyses and reports on progress in the implementation of the Sendai Framework for Disaster Risk Reduction 2015-2030, supports regional and national implementation of the Framework and catalyzes action and increases global awareness to reduce disaster risk working with UN Member States and a broad range of partners and stakeholders, including civil society, the private sector, parliamentarians and the science and technology community.


Position Summary

The Data Scientist will play a central role in developing and operationalizing analytical workflows that support UNDRR’s Global Assessment Reports (GAR) and related knowledge products, as well as broader risk metrics initiatives. This position is located in the Global Risk Assessment and Reporting Section of the Risk Knowledge Branch, and reports to the head of Section. The focus of this job is to use internal and external data for evidence-based decision support by looking at historical trends, current data and predictions, with a particular emphasis on assessing and modeling risk.

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