WELCOME TO SKILLLAB

At SkillLab, we envision a world where everyone has a pathway to employment. We enable and lead a radical change - a transition towards skill-based labor and education markets. Our AI-driven technology creates more inclusive and efficient markets by connecting people with jobs and education through the universal language of skills. SkillLab is working with employment and career services in 35+ countries and seeks to transform labor and education markets globally.
SkillLab’s innovative use of AI was recognised with awards like Google AI for Impact, UNESCO Top 10 uses of AI and the European Social Innovation Competition 2021, among other awards.

YOUR SKILLS

• Study background in Data Science, Computational Linguistics, Information Science, or a related field.
• Familiarity with Python, SQL, data analysis, and visualization (using tools like pandas, matplotlib, seaborn).
• Adept at statistical analysis, data exploration, pattern recognition, data cleaning, transformation, and quality assessment.
• Familiarity with taxonomies, ontologies, or classification systems and experience with data analysis, reporting, and visualization.
• Understanding of occupation classification systems (like ESCO, O*NET, ISCO), skills frameworks, competency models, labor market data, and employment terminology.

REQUIREMENTS

• You are eligible to work in the Netherlands. We don’t support visa applications for applicants outside the EU.
• You are currently enrolled as a student or graduated within the last 12 months.
• You are able to commit to a six-month full-time (40 hour/week) internship.

WHAT ELSE TO KNOW?

• This job description is intended to provide a general overview of the internship position and may be subject to change based on the organization’s requirements and priorities.
• If this job is not for you, perhaps you have a friend who might be a good fit. Send them this job posting!
• Get 4 tips for your job application at SkillLab

• Manual data classification & curation: Classify and curate occupation and skill data, ensuring quality and preparing it for AI models.
• AI/ML pipeline development: Build processes for bulk data, adding quality scores and validation checks for automated classifications.
• Standards research & implementation: Research international taxonomy standards and best practices, analyze gaps between SkillLab taxonomies and industry standards, recommend improvements and standardization approaches, and assist in mapping between different classification systems.
• Taxonomy enrichment & mapping: Enrich existing taxonomies with additional metadata, create cross-references between occupation and skill categories, develop hierarchical relationships and skill clusters, and map emerging occupations and skills to existing frameworks.