United Nations Office for Disaster Risk Reduction is looking for a Data Scientist (French-, Chinese-, Arabic-, Russian- or Spanish-speaking)

Org. Setting and Reporting

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. To accelerate risk-informed policy, investment, and private-sector engagement, UNDRR is seeking a highly skilled data scientist with strong technical expertise in statistical modeling and risk analytics. 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.

Responsibilities

• Work with stakeholders throughout the organization to identify opportunities for leveraging data and risk metrics to drive business solutions.
• Mine and analyze data from organization databases to drive optimization and improvement of programme development, advocacy and business strategies, including risk-informed approaches.
• Assess the effectiveness and accuracy of new data sources and data gathering techniques, particularly those relevant for risk analysis.
• Develop custom data models and algorithms to apply to data sets, including models for risk forecasting, exposure analysis or vulnerability estimation.
• Use predictive modeling to increase and optimize entity experiences, benefit realization and other business outcomes with attention to anticipating emerging risks.
• Develop organization A/B testing framework and test model quality.
• Coordinate with different functional teams to implement models and monitor outcomes including risk-related model outputs.
• Develop processes and tools to monitor and analyze model performance and data accuracy including the performance of risk models under different scenarios.
• Performs other duties as assigned.

Competencies

• Professionalism: Knowledge of the data analysis life cycle from ingest and wrangling to analysis and visualization to present findings. Excellent knowledge of statistical and computational methods, such as clustering, classification, correlation, dimension reduction, forecasting, machine learning, detecting outliers, and regression including their application for risk assessment and uncertainty analysis. Takes pride in the work for the organization and understands the impact that can be brought into the organization by allowing data-driven and evidence-based decisions. Ability to apply judgment in the context of assignments given, plan own work and manage conflicting priorities. Shows pride in work and in achievements; demonstrates professional competence and mastery of subject matter; is conscientious and efficient in meeting commitments, observing deadlines and achieving

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