Job profile demand understanding in international financial organizations: a natural language processing approach
The present work aims to create a Machine Learning model using unstructured text data in order to predict whether a position is prone to taking a longer Time to Fill than the overall average. As well as building an initial categorization of profiles within the organizations in which it was carrie...
Guardado en:
| Autor principal: | |
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| Formato: | Tesis de maestría |
| Lenguaje: | Inglés |
| Publicado: |
Universidad Torcuato Di Tella
2024
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| Materias: | |
| Acceso en línea: | https://repositorio.utdt.edu/handle/20.500.13098/12917 |
| Aporte de: |
| Sumario: | The present work aims to create a Machine Learning model using unstructured
text data in order to predict whether a position is prone to taking a longer Time to
Fill than the overall average. As well as building an initial categorization of profiles
within the organizations in which it was carried out, this will help to provide
insights that allow understanding both the demand and supply of different job
profiles using different Natural Language Processing and Unsupervised Machine
Learning techniques.
The processing of the text data will be done by using an open source Language
Model (LLM) in order to generate their corresponding document embeddings. |
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