  A job usually involves the application of several complementary or
synergistic skills to perform its required tasks. Such relationships are
implicitly recognised by employers in the skills they demand when recruiting
new employees. Here we construct a skills network based on their co-occurrence
in a national level data set of 65 million job postings from the UK spanning
2016 to 2022. We then apply multiscale graph-based community detection to
obtain data-driven skill clusters at different levels of resolution that reveal
a modular structure across scales. Skill clusters display diverse levels of
demand and occupy varying roles within the skills network: some have broad
reach across the network (high closeness centrality) while others have higher
levels of within-cluster containment, yet with high interconnection across
clusters and no skill silos. The skill clusters also display varying levels of
semantic similarity, highlighting the difference between co-occurrence in
adverts and intrinsic thematic consistency. Clear geographic variation is
evident in the demand for each skill cluster across the UK, broadly reflecting
the industrial characteristics of each region, e.g., London appears as an
outlier as an international hub for finance, education and business. Comparison
of data from 2016 and 2022 reveals employers are demanding a broader range of
skills over time, with more adverts featuring skills spanning different
clusters. We also show that our data-driven clusters differ from
expert-authored categorisations of skills, indicating that important
relationships between skills are not captured by expert assessment alone.
