  As job markets worldwide have become more competitive and applicant selection
criteria have become more opaque, and different (and sometimes contradictory)
information and advice is available for job seekers wishing to progress in
their careers, it has never been more difficult to determine which factors in a
r\'esum\'e most effectively help career progression. In this work we present a
novel, large scale dataset of over half a million r\'esum\'es with preliminary
analysis to begin to answer empirically which factors help or hurt people
wishing to transition to more senior roles as they progress in their career. We
find that previous experience forms the most important factor, outweighing
other aspects of human capital, and find which language factors in a r\'esum\'e
have significant effects. This lays the groundwork for future inquiry in career
trajectories using large scale data analysis and natural language processing
techniques.
