  Recruiters usually spend less than a minute looking at each r\'esum\'e when
deciding whether it's worth continuing the recruitment process with the
candidate. Recruiters focus on keywords, and it's almost impossible to
guarantee a fair process of candidate selection. The main scope of this paper
is to tackle this issue by introducing a data-driven approach that shows how to
process r\'esum\'es automatically and give recruiters more time to only examine
promising candidates. Furthermore, we show how to leverage Machine Learning and
Natural Language Processing in order to extract all required information from
the r\'esum\'es. Once the information is extracted, a ranking score is
calculated. The score describes how well the candidates fit based on their
education, work experience and skills. Later this paper illustrates a prototype
application that shows how this novel approach can increase the productivity of
recruiters. The application enables them to filter and rank candidates based on
predefined job descriptions. Guided by the ranking, recruiters can get deeper
insights from candidate profiles and validate why and how the application
ranked them. This application shows how to improve the hiring process by giving
an unbiased hiring decision support.
