Internship: LLM-Driven SPS Data & Observatory Metrics

WOAH; Paris; Vor Ort; EUR 6.800 - 9.200; Vollzeit

Zusammenfassung

WOAH invites a motivated student to join the Observatory team at the Paris headquarters for a six-month internship in data science, statistics and NLP. You will build data pipelines that extract structured information from SPS notifications and STCs, map them to WOAH Codes and Manuals, and triangulate results with national regulations to inform indicator definitions.

The role emphasizes basic statistics, uncertainty quantification, and collaboration with internal stakeholders.

Qualifikationen

• Enrollment in a Master’s degree (M1/M2) or final year of engineering school in a quantitative field.
• Experience with data processing and statistical analysis using R or Python.
• Interest in NLP, text extraction or LLM-based applications is a plus.
• Excellent command of English (written and spoken).
• Ability to work autonomously in a multidisciplinary team.
• Strong knowledge of data quality and reproducible code practices (Git, documentation).

Aufgaben

• Develop reproducible data pipelines to collect SPS notifications and STCs.
• Integrate LLMs to map notifications against WOAH Codes and Manuals.
• Document methods, pipeline, and results and present to DIAD colleagues.
• Triangulate outputs with national regulations and assess extraction accuracy.
• Contribute to scoping studies and basic statistics for indicator matrices.

Kenntnisse

Data processing

Statistical analysis

R or Python

NLP/LLM knowledge

English proficiency

Reproducible code

Ausbildung

Master’s degree in data science, statistics, CS or related quantitative field

Tools

R

Python

Jobbeschreibung

WOAH invites a motivated student to join the Observatory team at the Paris headquarters for a six-month internship in data science, statistics and NLP. You will build data pipelines that extract structured information from SPS notifications and STCs, map them to WOAH Codes and Manuals, and triangulate results with national regulations to inform indicator definitions.

The role emphasizes basic statistics, uncertainty quantification, and collaboration with internal stakeholders.