Internship - Data Analyst Multi-Disease Anomaly D...

Geneva Com; Paris; On-site; EUR 7,800 - 8,100; Full time

Benefits offered by this job

Stipend: 715 €/mo

Job summary

World Organisation for Animal Health (WOAH) in Paris invites a data analyst intern to build time series from official and open-source data streams for anomaly detection. You will benchmark methods across WA H I S and EIOS, develop a combined alert approach, and deliver a reproducible workflow with dashboards and automated alerts.

The role requires a master's level focus, familiarity with R/Python, SQL, Git, and dashboard tools; English communication is essential.

Qualifications

• Currently enrolled in, or recently graduated from, a master's degree (or equivalent) in Data Science, Biostatistics, Statistics, Applied Mathematics, or Epidemiology with a strong quantitative component.
• No professional experience required, but relevant experience through coursework, an academic project or a previous internship is expected.
• Basic knowledge of time series analysis; experience writing reproducible analytical code; basic dashboard development experience; understanding of how detection and surveillance systems are evaluated (sensitivity, PPV, timeliness).
• Technical skills: R and/or Python, SQL, Git, reproducible reporting (Quarto/R Markdown), dashboards (Shiny, Dash, Power BI). Good communication skills in English; other WOAH languages appreciated.

Responsibilities

• Build time series from WAHIS and EIOS suitable for anomaly detection by disease, country/territory and region.
• Benchmark anomaly detection algorithms across both streams and evaluate their ability to warn of the start of an epidemic wave, the approach of its peak and its post-peak decline.
• Compare EIOS and WAHIS anomalies and propose a combined alert rule.
• Consolidate the work into a reproducible, version-controlled and documented pipeline that WOAH staff can run, maintain, update and host, with automated data updates, quality checks and analytical reports (e.g. Quarto).
• Design and develop dashboards and automated alerts presenting early warning outputs.
• Produce methods documents, a user guide, a final report and a presentation of results to WOAH staff.

Skills

R

Python

SQL

Git

Quarto

Shiny

Dash

Power BI

Time series

Dashboard

English communication

Education

Master's degree in Data Science or related fields

Tools

R

Python

SQL

Git

Power BI

Shiny

Dash

Quarto

Job description

Internship - Data Analyst Multi-Disease Anomaly Detection System Using Wahis Official Data and Eios Open-Source Information

World Organisation for Animal Health (WOAH)

Location: Paris, France

Apply by: 04 Nov 2026

Administration, HR, Management, Accounting/Finance

Internship - Data Analyst Multi-Disease Anomaly Detection System Using WAHIS Official Data and EIOS Open-Source Information

The World Organisation for Animal Health (WOAH) is an intergovernmental organisation representing 183 Members worldwide, working to improve animal health, animal welfare and Veterinary Services. WOAH's headquarters are in Paris, and the Organisation has 13 Regional or Sub-regional Representations.

Job purpose: WOAH collects official animal disease information from its Members through the World Animal Health Information System (WAHIS) and complements it with open-source signals through the Epidemic Intelligence from Open Sources (EIOS) initiative coordinated by WHO. WOAH has built a proof of concept for anomaly detection (Epidemic Volatility Index and Convergence Epidemic Volatility Index) on High Pathogenicity Avian Influenza and now intends to move to a multi-disease anomaly detection tool exploiting both WAHIS official data and EIOS open-source information for all WOAH listed diseases. The intern will identify, benchmark and evaluate anomaly detection methods on trends derived from both streams, combine the signals, develop the resulting early warning workflow, and make its outputs available in a dedicated tool for WOAH and eventually its Members.

Objectives and activities:

• – Build time series from WAHIS and EIOS suitable for anomaly detection by disease, country/territory and region.
• – Benchmark anomaly detection algorithms across both streams and evaluate their ability to warn of the start of an epidemic wave, the approach of its peak and its post-peak decline.
• – Compare EIOS and WAHIS anomalies and propose a combined alert rule.
• – Consolidate the work into a reproducible, version-controlled and documented pipeline that WOAH staff can run, maintain, update and host, with automated data updates, quality checks and analytical reports (e.g. Quarto).
• – Design and develop dashboards and automated alerts presenting early warning outputs.
• – Produce methods documents, a user guide, a final report and a presentation of results to WOAH staff.

Requirements:

Currently enrolled in, or recently graduated from, a master's degree (or equivalent) in Data Science, Biostatistics, Statistics, Applied Mathematics, or Epidemiology with a strong quantitative component. No previous professional experience required, but relevant experience through coursework, an academic project or a previous internship is expected. Basic knowledge of time series analysis; experience writing reproducible analytical code; basic dashboard development experience; understanding of how detection and surveillance systems are evaluated (sensitivity, positive predictive value, timeliness). Technical skills: R and/or Python, SQL, Git, reproducible reporting (Quarto/R Markdown), dashboards (Shiny, Dash, Power BI), scheduled job automation. Good communication skills in English; other WOAH languages appreciated. Familiarity with anomaly detection, machine learning for time series, and animal health surveillance are assets.

Conditions:

Full-time, 6 months, based at WOAH Headquarters, 12 rue de Prony, 75017 Paris, France. Stipend: 715 € / month. Start as soon as possible, and no later than 4 January 2027. Requires an agreement with the student's academic institution.