Data Scientist

Overview

Lead data science initiatives to enhance forecasting systems for humanitarian response.

You have:

• Degree in a field related to machine learning, artificial intelligence, human interaction systems, applied mathematics, computer engineering, telecommunications engineering, computer sciences (or similar) or remote sensing, image or signal processing.
• Excellent programming skills (e.g., Python, C/C++, etc.).
• Experience in machine learning/deep learning methods, such as LSTM, GNN, and Transformer.
• Solid understanding of machine learning/deep learning methods, statistical modelling, and optimization techniques; experience in developing time-series forecasting systems.
• Experience in data mining.
• Skills in presenting scientific research, writing papers in scientific journals, and crafting technical reports.
• Fluency in English.
• A minimum of 6 years of relevant work experience with Bachelor's degree or equivalent.
• Ability to work both independently and collaboratively in an international team.

Deadline for Applications

December 29, 2025Hardship Level (not applicable for home-based)

H (no hardship)Family Type (not applicable for home-based)

FamilyStaff Member / Affiliate Type

CONTR International Senior Level ProfessionalTarget Start Date

2026-01-19Terms of Reference

ATTENTION: THIS POSITION IS BASED IN LUXEMBOURG

I. PROJECT DETAILS

Requesting office (innovation Service): DEPS (Innovation Service Project)

Title of project: Data Scientist (P3), Early Warning and Effective Response System Project

Duration: As soon as possible until 30/06/2026 (with possibility of extension)

Purpose of project (summary): The project aims to enable UNHCR and partners in country operations to: 1) improve their capacity to forecast humanitarian emergencies and the likelihood/impacts of displacement through country and context-specific models, 2) enhance informed investment planning for resilience-strengthening efforts with local communities, 3) make time-sensitive and data-informed decisions to prepare for and respond to emergencies through strengthened multi-stakeholder cooperation.

Assignment and/or Mission travel:

Applicable to High or Very High-Risk duty stations Specify destination(s), period(s) and allocated amounts: Possibly to pilot countries for no longer than 2 weeks at a time.

Applicable to non-High-Risk duty stations Specify destination(s), period(s) and allocated amounts: Possibly to pilot countries for no longer than 2 weeks at a time.

Position Location (duty station): Luxembourg Institute of Science and Technology Premises, Esch-Sur-Alzette, Luxembourg, for the duration of the contract.

II. DESCRIPTION OF ASSIGNMENT, DUTIES AND RESPONSIBILITIES OF THE ENGAGED INDIVIDUAL CONTRACT HOLDER

General Background of Project or Assignment, Operational Context:

In line with the 2030 Agenda for Sustainable Development and the principle of “Leave No One Behind,” UNHCR aspires to develop, together with strategic partners, a people-centred global Early Warning and Effective Response System (EWERS) that will systematically collect timely and effective data and information on potential risks and threats related to forced displacement caused by conflict and/or natural hazards. The EWERS will provide credible and actionable information and analysis at high frequency and high geographical resolution, which could:

• enable UNHCR and partners in country operations to improve their capacity to predict humanitarian crises and the likelihood/impacts of displacement through a country and context specific tool.

• ensure UNHCR and the partners/stakeholders with whom UNHCR shares the alerts will be able to conduct informed investment planning for strengthening resiliency efforts within local communities, as well as make time-sensitive and data-informed decisions to prepare for and respond to crises through strengthened multi-stakeholder international cooperation; and

• allow UNHCR and partners to harness emerging technologies and tools, such as nowcasting, forecasting and machine learning-based models, for anticipatory humanitarian action.

The project is led by the Preparedness Section within the Division of Emergency and Programme Support, in coordination with Innovation Service and Global Data Service. The project is implemented in coordination with the Luxembourg Institute of Science and Technology (LIST).

Occupational Safety and Health Considerations To view occupational safety and health considerations for specific duty stations, please visit this link: https://wwwnc.cdc.gov/travel

Purpose and Scope of Assignment:

Responsibilities The Data Scientist will work closely with UNHCR colleagues, the LIST team and external parties. S/he will report to the Senior Project Manager and assume following responsibilities in coordination with LIST scientists:

Research and preparation: • Conduct research to analyse externally available early warning models to build displacement models. • Conduct research in artificial intelligence and explore algorithms and methodologies. • Assess displacement risks across temporal and spatial dimensions in relation to potential triggering events. • Identify pilot countries and focus area. • Identify data requirement and their availability. • Identify open data sources. • Establish data requirements. • Analyse potential risks in relation to data and develop mitigation strategies.

Design and development • Develop machine learning models to ingest multi-source data, including publicly available datasets on hazard, exposure, and vulnerability. • Design multivariate time series based early warning system by exploring modern deep learning models, such as LSTM, GNN, and Transformer. • Develop uncertainty quantification methods to calibrate and estimate uncertainty of forecast. • Develop (deep) causal inference/discovery algorithms to uncover causes and drivers of early warning.

Data collection • Develop processes to extract, clean, and analyse datasets. • Collect, clean, store, and organize data in line with UNHCR’s Data Management Guidelines. • Create and manage a directory in UNHCR designated cloud services to safeguard the data collected. • Categorize, update, track and analyse data. • Create data blocks to be automated and included in future applications. • Document meta data.

Pilot, evaluation and rollout • Model evaluation and debugging. • Analyse and incorporate end user feedback in relation to the displacement forecasting and user interface application. • Pursue deployment of the system. • Develop and maintain documentation of processes, findings and achievements for publication in scientific journals.

III. CANDIDATE MINIMUM REQUIREMENTS, SELECTION CRITERIA

Required qualifications, language(s) and work experience:

Degree in a field related to machine learning, artificial intelligence, human interaction systems, applied mathematics, computer engineering, telecommunications engineering, computer sciences (or similar) or remote sensing, image or signal processing.

Required/mandatory: • Excellent programming skills (e.g., Python, C/C++, etc.). • Experience in machine learning/deep learning methods, such as LSTM, GNN, and Transformer. • Solid understanding of machine learning/deep learning methods, statistical modelling, and optimization techniques Experience in developing time-series forecasting systems. • Experience in data mining. • Skills in presenting scientific research, writing papers in scientific journals, and crafting technical reports. • Communicative and willing to learn, self-organized, and creative. • Ability to work both independently and collaboratively in an international team. • Fluency in English.

Desirable: • Master’s (with five years of relevant experience)/PhD (with four year of relevant experience) in a field related to machine learning, artificial intelligence, human interaction systems, applied mathematics, computer engineering, telecommunications engineering, computer sciences (or similar) or remote sensing, image or signal processing. • Knowledge on forced population displacement. • Knowledge on database technologies (e.g. CouchDB, SQL). • Experience in Earth Observation (EO) data processing; experience with data visualization technologies (e.g. Tableau, RShiny, MarkdownR, MS Vision, MS InDesign/Visual Studio); familiarity with commercial artificial intelligence and machine-learning software (e.g. Watson, Crimson Hexagon, Eureqa, etc); experience with cloud computing services.

Minimum years of work experience (NOTE: candidates with less years of experience cannot be short-listed or recommended): A minimum of 6 years of relevant work experience with Bachelors degree or equivalent; or a minimum of 5 years of relevant work experience with Masters degree or equivalent; or a minimum of 4 years of relevant work experience with doctorate or PhD equivalent.

IV. EVALUATION, MONITORING, SUPERVISION

Monitoring and Progress Controls (Performance Meassures)

• Appropriate data was identified, collected, and integrated into AI models for accuracy and reliability. • Oversaw the development of models addressing various displacement drivers in collaboration with the LIST project team • Maintain daily communication with the Senior Project Manager and the LIST team members to track progress and ensure effective coordination. • Conduct periodic reviews and discussions with the UNHCR-LIST project team to align tasks, responsibilities, and project direction.

• Continuously document sources, resources, development processes, results, and key decisions for transparency and accountability.Standard Job Description

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This position doesn't require a functional clearance Remote

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Added 6 months ago - Updated 6 months ago - Source: unhcr.org