Compare job ads with a given set of categories (which each consists of terms)
In terms of data understanding, I would recommend an unsupervised approach first. For example you could ...


.. perform tf/idf vectoring and build a simple tag cloud
.. perform Latent Dirichlet Allocation to get an overview of latent topics in your data (like clustering and interpret the categories)
.. train a word embedding (e.g. word2vec) on top of your data. Then perform a dimension reduction (PCA) to visually explore your data. (You could find semantically and syntactically clusters)


After the unsupervides data exploration you could perform a text classification based on classical approaches (e.g. bag of word models) or with neural nets (=supervised).