Job Vacancies Analytics


Brief:

To enable clients to understand the demand for key professions and better assess their local skills-base by creating a continually updating dashboard for the UK jobs market.


Approach:

The data streams are focused towards a user base that would typically read a single advert at a time. This means that categorisation of the data in to Office of National Statistics Standard Occupation Codes is required. Given that the numbers of records exceeds 1 million and is growing daily, this is not possible manually and an automated solution is required. We developed an automated classifier algorithm using a random forest decision tree Machine Learning approach. Based on our back testing, the classifier is operating at greater than 99% accuracy of classification of a job based on the job advert text alone. In addition to this classifier, we also developed the capability to extract additional information about each job, such as employer, location, time of advertisement and, if available, salary being offered. Repeated application allows us to generate time series and comparative analysis of jobs in a variety of area types (LEP, Local Authority as well as custom areas) across the whole of the UK.


Conclusion:

We have created a Jobs Vacancies Dashboard which, using a combination of Machine Learning and text analysis to extract information from free text job adverts, provides up-to-date information about hiring in any locality in the UK. This valuable intelligence describes the skills-base of an area, allows gap analysis to be undertaken and provides competitive intelligence about organisations by monitoring who they are hiring.