Internship – Data Analyst

WOAH; Paris; On-site; EUR 6,800 - 9,200; Full time

Job summary

WOAH HQ in Paris is offering an internship for a Data Analyst to develop an anomaly detection system using WAHIS official data and EIOS open sources. You will build time series, benchmark algorithms, and help design dashboards and automated alerts for internal WOAH use.

The role emphasizes reproducible code, documentation, and the ability to work with official data streams to detect anomalies across diseases, contributing to an early warning workflow.

Qualifications

• Education: master’s degree in Data Science, Biostatistics, Statistics, Applied Mathematics, or Epidemiology.
• Experience: not required; coursework or academic project acceptable.
• Basic knowledge of time series analysis and reproducible analytic coding.

Responsibilities

• Build time series from WAHIS and EIOS for each disease and country/region.
• Benchmark anomaly detection algorithms across WAHIS and EIOS data.
• Evaluate performance against reported events (early warning, peak, post-peak).
• Compare signals from EIOS and WAHIS and design integrated alerts.
• Develop a reproducible pipeline and dashboards for WOAH use.

Skills

Programming in R and/or Python

SQL querying

Git version control

Reproducible reporting (Quarto or R MD

Dashboard development (Shiny, Dash, BI

Automated task scheduling

Data management practices

Code documentation and structuring

Education

Master’s degree in Data Science, Biostatistics, Statistics, Applied Mathematics, or Epidemiology

Tools

SQL

Git

Job description

Internship – Data Analyst multi-disease anomaly detection system using WAHIS official data and EIOS open-source information, DIAD – WOAH HQ

Paris, France

Context

The World Organisation for Animal Health (WOAH – founded as OIE) is a leading intergovernmental organisation representing 183 Members worldwide. Through its activities, WOAH makes a decisive contribution to improving animal health, protecting animal welfare and strengthening Veterinary Services. The Organisation provides transparent information on world’s animal health situation, and promotes international standards, particularly in terms of the safety of trade in live animals and animal products. More information can be found on WOAH’s website.

Joining WOAH means taking part in the development of one of the leading international organisations, recognised and associated with other multilateral institutions, in the field of worldwide health. It means helping to build a global approach to health, combining animal and human health in a “One Health” approach. It means joining teams motivated by the impact of their actions, the sense of their collective commitment and their recognised professionalism in their respective fields of expertise.

WOAH’s headquarters are based in Paris. The Organisation is present on every continent through 13 Regional or Sub-regional Representations. WOAH has 250 staff members, two-thirds of whom are based at headquarters.

Job Description

Positioning and reporting

The Intern will report to Paolo Tizzani ([e-mail]) and Soline Chaumien-Tabouis ([e-mail])

Job purpose

Since its foundation, WOAH has been collecting information on animal diseases. As knowledge of pathogens and diseases is crucial for animal and human health, WOAH asks all its Members to report the occurrence of specific pathogens and diseases on the official list in a timely and complete manner. This information is collected and displayed on a specific public interface, the World Animal Health Information System (WAHIS). WAHIS collects official information from 183 WOAH Members and around 20 non-Members, providing a global snapshot of the animal health situation, with a continuous series of disease events available since 2005. WOAH complements this official information with signals detected from open sources (media, official bulletins, specialised websites). This open-source screening, known as event-based surveillance, allows WOAH to detect potential animal disease events at an early stage and to verify and complement official reporting. To carry it out, WOAH takes part in the Epidemic Intelligence from Open Sources (EIOS) initiative, coordinated by the World Health Organization. WOAH’s epidemic intelligence therefore relies on two complementary streams of information: official data reported by Members through WAHIS, which are robust and reliable information but may be subject to reporting delays and to the heterogeneity of national surveillance systems; and non-official information collected mainly through EIOS, which is timelier but unverified, not always reliable, noisier and dependent on media coverage. Detecting, as early as possible, anomalies of the epidemiological situation from its expected behaviour would allow WOAH, its Members and its partners to anticipate epidemic waves, and to target risk communication, preparedness and capacity building activities accordingly. WOAH’s objective is to develop an anomaly detection system that exploits both streams and is applicable across all the listed diseases. This objective is part of the framework developed by WOAH for its epidemic intelligence activities, under the activity dedicated to the detection of temporal aberrations for early warning and rapid reaction. This activity foresees the validation of an analytical approach able to warn of i) the beginning of an epidemic wave, ii) the approach of the epidemic peak and iii) the decreasing post-peak trend of the wave, using trend series from both official data from WAHIS and unofficial information from EIOS. WOAH tested this approach on High Pathogenicity Avian Influenza (HPAI). WOAH developed, a proof of concept applying two anomaly detection analysis to the HPAI outbreaks reported in poultry to WAHIS between 2005 and 2024: the Epidemic Volatility Index (EVI) and the Convergence Epidemic Volatility Index (cEVI). This work has produced a set of assets that will be made available to the intern. WOAH now intends to move from this proof of concept to the implementation of a, multi-disease anomaly detection tool, exploiting both WAHIS official data and EIOS open-source information, for all WOAH listed diseases. The overall mission of the intern will therefore be to identify, benchmark and evaluate anomaly detection methods applicable to trends derived from WAHIS official data and to EIOS open-source information for all WOAH listed diseases, to combine the signals derived from both streams, to develop the resulting early warning workflow, and to make its outputs accessible in a specific tool for WOAH and, eventually, its Members.

• To build, from WAHIS and EIOS, time series suitable for anomaly detection by disease, and country/territory and region.
• To benchmark a range of anomaly detection algorithms across both WAHIS and EIOS time series.
• To evaluate the performance of these methods against the events observed, by disease, including their capacity to warn of the beginning of an epidemic wave, of the approach of its peak and of its post-peak decrease.
• To compare the anomalies derived from EIOS and from WAHIS, and to propose a combined alert rule exploiting the timeliness of open-source information and the reliability of official data.
• To consolidate and develop the analytical work into a reproducible, version-controlled and documented pipeline, designed to accommodate additional diseases, that WOAH staff can run, maintain, update and host on WOAH infrastructure.
• To design and develop dashboards and automated alerts presenting the early warning outputs, for internal WOAH use and, where relevant, for Members.

Missions and activities

Inception: material and methods

• A document describing the anomaly detection methods retained for the benchmark, with the rationale for their selection and their exclusion, building on the review of the literature made available by WOAH.
• A document describing the technical approach retained to consolidate and develop the pipeline, covering architecture, languages, version control, hosting, and the execution and update procedure.
• A document describing the development of dashboards and the alert products.
• A list of the data, accesses and infrastructure needed to apply the methodology selected.
• A work plan setting out the timeline for the deliverables.

Construction of time series from WAHIS and EIOS

• A characterisation of the WAHIS and EIOS data available for all the listed diseases, covering volume, granularity, and temporal and geographical coverage.
• A clean Masterfile of the WAHIS and EIOS time series, documented and reproducible.

Benchmarking of anomaly detection algorithms

• The implementation of the anomaly detection methods retained, applied to the WAHIS and EIOS time series for all the diseases.
• A comparative analysis of the performance of these methods by disease, and by stream of information.
• A recommendation on the anomaly detection strategy to be retained, stating whether a single generic method with disease-specific parameters or different methods according to the epidemiological profile should be used.

Consolidation and development of the pipeline

• A single version-controlled repository containing the consolidated code for the anomaly detection, the evaluation and the production of the alerts, with a documented structure allowing additional diseases to be integrated.
• A reproducible execution environment, with documented dependency management and run procedure, allowing the analysis to be re-run on updated data.
• The automation of the data update, of the execution of the models and of the publication of the results, with automated quality checks on the input data and on the outputs of the pipeline.
• Automated analytical reports, for example produced with Quarto, regenerated directly from the pipeline.
• A documented procedure for adding a new disease to the early warning system.
• A documented procedure for the deployment, the update, the monitoring and the debugging of the application on WOAH infrastructure.

Dashboards, alerts and visualisation products

• One or more dashboards presenting the anomaly detection outputs derived from WAHIS and EIOS side by side, by disease, host category, region, Member and period, including the configuration of the model parameters and of the alert rule.
• An automated alert product notifying WOAH staff of the signals meeting the agreed decision rule.
• The source code and the documentation of the dashboards, in a form that can be deployed and maintained by WOAH.
• A user guide for the intended audiences.

Final reporting

• A final report summarising the work carried out, the results obtained, the limitations identified and the recommendations for the routine operation of the early warning system, including its possible extension to further diseases and data sources.
• A presentation of the results to WOAH staff.

Requirements

Qualifications and Experience

• Education: 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 is required. However, the intern should have gained some relevant experience through coursework, an academic project, or a previous internship.
• Basic knowledge of time series analysis.
• Experience in writing reproducible analytical code; basic scripting skills for data extraction or process automation.
• Basic experience in developing dashboards or interactive data visualisations.
• Understanding of how the performance of detection and surveillance systems is evaluated, including metrics such as sensitivity, positive predictive value, and timeliness of detection.
• Ability to clearly document analytical methods and communicate results in writing.
• Familiarity with anomaly detection methods would be an asset.
• Basic understanding of machine-learning approaches applicable to time series would be an asset.
• Basic knowledge of epidemiology or health surveillance would be an asset; knowledge of animal health surveillance and transboundary animal diseases would be an additional asset.

Required Skills

• Technical Skills
  • Programming in R and/or Python;
  • querying of relational databases (SQL); use of version control (Git);
  • reproducible reporting (e.g. Quarto or R Markdown);
  • familiarity with development of dashboards (e.g.Shiny, Dash, Power BI or equivalent);
  • knowledge of scheduled job automation, sufficient to deploy, update and debug the application;
  • good data management practices;
  • and the ability to document and structure code.
• Interpersonal Skills
  • Good computational skills
  • organisational skills
  • good communication skills in English (other WOAH languages appreciated but not required)
  • flexibility, capacity to work on short deadlines and to deliver upon schedule.

The intern will ensure that all pieces of information handled during the internship are treated with the highest level of confidentiality and that sensitive information will never be disclosed. All information treated or collected during this internship, including all code produced, remains under WOAH’s ownership.

Additional Information

Working conditions

Full‑time position based at WOAH Headquarters in Paris.

• Salary: 715 € / month
• Duration: 6 months
• Start date: as soon as possible
• Location: WOAH Headquarters, 12 rue de Prony 75017 Paris, France

Eligibility and start date

This internship requires an agreement with the student’s academic institution. The selection of the candidate and the internship start date will depend on the suitability of the candidate’s profile for the position, as well as on the operational and funding constraints associated with the internship. The selected candidate must be able to commence the internship in person in Paris, France, no later than 4 January 2027, to ensure that the internship activities can be carried out within the applicable funding period.