  We use algorithmic and network-based tools to build and analyze the bipartite
network connecting jobs with the skills they require. We quantify and represent
the relatedness between jobs and skills by using statistically validated
networks. Using the fitness and complexity algorithm, we compute a skill-based
complexity of jobs. This quantity is positively correlated with the average
salary, abstraction, and non-routinarity level of jobs. Furthermore, coherent
jobs - defined as the ones requiring closely related skills - have, on average,
lower wages. We find that salaries may not always reflect the intrinsic value
of a job, but rather other wage-setting dynamics that may not be directly
related to its skill composition. Our results provide valuable information for
policymakers, employers, and individuals to better understand the dynamics of
the labor market and make informed decisions about their careers.
