Socios
Context & overview
In a rapidly changing labour market, conventional forecasting methods often fail to capture emerging needs in Europe. To address this, institutions in some European countries launched a collaborative initiative connecting employment agencies, universities and data science centres. The goal was to design an AI-driven pipeline able to detect, extract and classify skills in real time useful for training providers and policymakers.
Challenge addressed
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Persistent mismatch between supply and demand of skills. |
Vast volumes of unstructured data difficult to understand with traditional tools. |
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Slow feedback cycles, as policy reports often describe yesterday’s needs instead of the ones of tomorrow’s. |
Fragmentation of classifications (ESCO, national systems) which limits cross-border comparability. |
Solution implemented
A DataOps pipeline was implementing. It integrates data engineering and machine learning. The major steps included an integration of heterogeneous data such as vacancy portals and CV databases, a machine learning application to identify both technical and transversal skills and the mapping of competences extracted from European tools like ESCO.
Impact & results
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Accuracy |
Skill extraction achieved over 80% accuracy and above 94% recall. |
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Efficiency |
Automated processing reduced analysis time from days of manual coding to minutes through the use of AI. |
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AI’s support |
AI provide powerful signals, but expert interpretation remains essential. |
Skills extraction, labour market intelligence, DataOps pipeline, machine learning, ESCO integration
