Using A.I to make recommendations for career progression

Despite the wealth of information available to job seekers, choosing careers and transitioning
between jobs remain somewhat random. With thousands of job titles available, it is difficult
for candidates to know what each role entitles and how well-suited they are for various
positions.
Our research aims to break though this complexity and identify the most fitting careers for
every job. To this end, we combine an exhaustive occupational dataset with multivariate
matching algorithms. These algorithms account for the semantic similarity between job
descriptions and for the overlap in the skills required by different jobs.
We find that a significant number of careers lie outside the field in which candidates currently
operate. Furthermore, we define a degree of specialization and use this to quantify the
number of paths open to every job. Finally, we cluster jobs and skills to reduce career map
complexity and obtain several insights related to career progression.