Data Scientist

Develops and modernises WIPO’s statistical and analytical products to deliver transparent, internationally comparable intellectual-property statistics. The data scientist builds dashboards, data platforms and analytics pipelines, applies machine learning and MLOps, and turns large-scale data into actionable insights that inform policy, operational and growth decisions.

• Implement and enhance internal and external statistical reporting tools
• Build and maintain web dashboards, data-access platforms and analytics pipelines
• Apply machine learning, LLMs and predictive modelling to statistical-data projects
• Analyse data and translate findings into actionable insights for Member States and senior management
• Apply MLOps practices for model development, deployment, monitoring and reproducibility
• Develop metrics, analytical models and AI solutions across project lifecycles
• Modernise statistical databases and data platforms for secure storage and retrieval
• Promote data-management, model-governance and analytics best practices

• First-level university degree in statistics, mathematics, data science, computer science, economics or a related discipline
• At least six years of progressively responsible professional experience in data analytics, data science, machine learning or a related field
• Experience using unit-record databases to generate actionable intelligence for client decision making
• Experience using statistical and econometric tools
• Experience applying data science and machine-learning techniques to large-scale statistical databases and data platforms
• Experience in advanced data preparation, validation, analysis, modelling and quality assurance
• Excellent written and spoken English

• Professional experience in intellectual property
• Experience deploying and maintaining production analytical models and data pipelines with version control, monitoring and reproducibility
• Good knowledge of intellectual property

• Data Science
• Statistical Analysis
• Machine Learning
• Predictive Modelling
• Data Preparation
• Data Validation
• Data Modelling
• MLOps
• Data Governance
• Data Visualisation
• Forecasting
• Quality Assurance

• SQL
• R
• Python
• STATA
• LLMs

• Temporary appointments may be renewed up to a maximum cumulative length of two years, subject to continuing needs, budget and satisfactory performance
• The deadline is indicated in the applicant's local time