Quantitative Epidemiologist / Data Scientist - Data Integration and Analytics Department

Quantitative Epidemiologist / Data Scientist - Data Integration and Analytics Department

World Organisation for Animal Health (WOAH)

Location: Paris, France

Apply by: 04 Aug 2026

Administration, HR, Management, Accounting/Finance

Overview The World Organisation for Animal Health (WOAH) is a leading intergovernmental organisation representing 183 Members. WOAH aims to improve animal health, protect animal welfare, and strengthen veterinary services. WOAH provides transparent information on the world’s animal health situation and promotes international standards, including in trade safety for live animals and animal products. WOAH is strengthening its Epidemic Intelligence (EI) Framework to build a more anticipatory, data-driven approach that integrates advanced data analytics, AI, machine learning, and robust forecasting into EI processes. WOAH’s headquarters are in Paris, with 13 regional or sub-regional representations. The position is located in Paris.

Job description

Reporting This position reports to the Deputy Director, Standards Setting and Implementation, and to the Head of the Data Integration and Analytics Department (Gilles Guillot, ). Under the supervision of the Head of the DIAD and within the DIAD Epidemic Intelligence team, the staff will conduct the following activities:

• Epidemic Intelligence Framework implementation
  • Contribute to operationalising the WOAH Epidemic Intelligence Framework by translating strategic priorities into analytical processes, deliverables, and workflows.
  • Develop and maintain protocols to integrate official data sources (e.g., WAHIS) with non-official sources (e.g., media scanning, social media, news aggregators).
  • Support horizon-scanning methodologies to identify, assess, and rank emerging threats at the human–animal–environment interface.
• Forecasting and AI integration
  • Lead the design and implementation of a structured forecasting methodology embedded within WOAH EI processes, including selection, validation, and documentation of quantitative methods.
  • Apply and evaluate machine learning and statistical modelling approaches for risk estimation (e.g., disease emergence, geographic spread, impact).
  • Contribute to responsible AI integration into analytical workflows, including governance (model explainability, bias assessment, uncertainty quantification).
• Early warning and situational awareness products
  • Support the production of regular early warning and situational awareness products integrating official and non-official data sources.
  • Support development and maintenance of dashboards providing real-time or near-real-time visibility on animal health events, trends, and risk indicators.
  • Contribute to data standards, shared taxonomies, and interoperability frameworks to improve consistency and comparability of outputs across WOAH and partners.

Expected impact

• Shift WOAH’s operational activities from reactive detection to anticipatory risk-based decision-making.
• Embed forecasting and AI-driven analytics within core EI processes, shortening the time between signal identification and strategic decisions.
• Improve resilience to complex threats by modelling multi-driver scenarios (climate, trade, land use, pathogen spread) for better preparedness.
• Strengthen One Health surveillance through tighter integration of animal, human, and environmental health intelligence.

Requirements

Qualifications and experience

• Essential: Advanced degree (MSc or PhD) in data science, biostatistics, epidemiology, computational biology, or related quantitative field.
• At least 10 years’ experience in forecasting and predictive analytics, including time-series modelling, machine learning-based risk prediction, probabilistic risk assessment, or scenario-based modelling.
• Proficiency in at least one analytical programming environment (e.g., Python, R) and familiarity with data pipelines, API-based data ingestion, and reproducible workflows.
• Experience with international organisations, national governments, or global health initiatives, preferably with One Health, animal health, or zoonotic disease experience.

Desirable skills

• Experience in epidemic intelligence: systematic collection, integration, and analysis of heterogeneous data sources to detect and assess threats early.
• Familiarity with AI/ML frameworks applied to epidemiological or environmental data.
• Geospatial analysis and visualization experience.
• Knowledge of NLP or event-based surveillance for informal signal detection.
• Experience designing or contributing to early warning dashboards and decision-support tools (interactive data visualization such as Power BI, Tableau, R Shiny, Plotly Dash).
• Proven ability to communicate complex analytical outputs to non-specialist audiences, including senior decision-makers and Member governments.

Application

For complete job description and to apply, visit:

HOW TO APPLY: If you are interested in the position, please complete your application online by August 4th, 2026. All applications will be reviewed after the closing date of the publication. Apply here: