Associate Data Scientist

Hardship Level (not applicable for home-based)

H (no hardship)

Family Type (not applicable for home-based)

Family

Staff Member / Affiliate Type

UNOPS IICA1

Target Start Date

2025-04-01

Deadline for Applications

February 3, 2025

Terms of Reference

Title: Associate Data Scientist
Duty station: Copenhagen, Denmark (Remote work possible)
ICA Level: IICA-1/LICA-9
Corresponding level: P-2/NO-B
Duration: from 01/04/2025 to 31/12/2025 (with possibility of extension)

General Background

The Statistics, Data Science, and Survey Section (SDSS) which is located in UNHCR’s Global Data Service (GDS) in Copenhagen, develops statistical definitions and standards, oversees the production of global statistics on refugees and other forcibly displaced populations, and applies innovative methods to answer questions related to forced displacement. Access to reliable, timely and accurate data is essential to achieve the aims of the Data Transformation Strategy 2020-2025, that is to position UNHCR as a trusted leader on data and information related to refugees and other populations of concern. The Section develops external products such as the annual Global Trends report, which analyzes the changes in forced displacement and statelessness and deepens public understanding of ongoing crises, and the Refugee Data Finder, which enables humanitarian workers, decision-makers, researchers and many more to quickly access data and information on affected populations. The Section also uses innovative data sources and methods to help solve some challenges faced by the current and future state of forced displacement.

Purpose and Scope of Assignment

The position is based in Copenhagen with the Statistics, Data Science, and Survey Section of the Global Data Service and will have a duration of 18 months. The Associate Data Scientist will work as member of the Data Science Team of the Statistics, Data Science, and Survey Section to contribute to the delivery of the project: “Transfer Learning for High-Resolution Socio-Economic Data’, which aims to generate high-resolution socio-economic indicators for refugees and host populations in Sub-Saharan Africa through deep learning. The project is funded by the World Bank-UNHCR Joint Data Center and will be a collaboration between UNHCR in the lead, the AI and Global Development Lab at Chalmers University, and the World Bank as supporting partners. The successful candidate is expected to work in close collaboration with all three organizations.

Duties and Responsibilities
This position offers an exciting opportunity to:

1. develop a novel approach to estimate socio-economic indicators for forcibly displaced populations and their host populations using remote sensing imagery and survey data;
2. under the guidance of the academic project partners, use transfer learning to adapt existing deep learning model developed by the AI and Global Development Lab at Chalmers University to forcibly displaced populations;
3. in collaboration, with the data science and survey experts in UNHCR and the World Bank, scale up the approach to publish newly developed indicators:
4. work with a team of academic partners, and other data scientists and data engineers within the humanitarian and development sector;
5. work closely with UNHCR field operations to ensure the relevance and applicability of the approach.

The candidate will be expected to engage with and support the following activities:
• Develop a transfer learning model to estimate DHS wealth index components for refugee and host populations using remote sensing imagery.
• Adapt existing deep learning architectures under guidance from academic partners, UNHCR’s data scientist and survey experts.
• In collaboration with academic partners and survey experts, write a methodological article on the approach.
• Participate in technical panels, workshops, conferences, and policy briefs based on the project’s results.
• Work in close collaboration with UNHCR and World Bank country offices.

Monitoring and Progress Controls

The candidate will be expected to:
• develop a transfer learning model with a specific focus on refugee and host populations based on the existing work of the AI and Global Development Lab using satellite imagery,
• participate in and contribute to the project’s technical advisory group,
• contribute to the setup of a satellite imagery database and data pipelines for data cleaning and results dissemination,
• write policy briefs and an academic article on the methodology and results,
• set up and maintain functioning communication channels with field colleagues.

Qualifications and Experience

Education
University degree in Data Science, Comput