Using Data Science to help people get back into work
DataSparQ recently worked with Reed, Code First Girls and Google as part of the Emergent Alliance to help people get back into work post Covid.
One of the challenges put to the Emergent Alliance by Reed was to assess ways in which job and skills data might be used to help those made redundant during the pandemic get back into work.
Figures from the ONS show UK unemployment at 4.9% in April 2021 (up from 4% a year earlier) with hospitality, retail and entertainment sectors especially impacted. Of around 800,000 payroll jobs to disappear since March 2020, 526,000 are from retail and hospitality alone.
What’s more, for some specific sectors like high street retail, it’s not clear how quickly (if ever) employment levels will return to their pre-covid levels.
For those who have been made redundant through this period there are a couple of significant challenges Reed identified:
(a) People whose sector or industry has been significantly disrupted may not know where to look for their next role
and
(b) they may be missing key skills which would allow them to move into other roles or sectors (and may not be aware what those skills are).
We set out to develop a proof of concept (PoC) that would demonstrate how job and skills data can be used to help solve these problems. Head over to the Job Finder Machine to have a play.
So what’s going on here? To satisfy challenge (a), we wanted to reimagine job search as a less targeted activity. For example, rather than asking a search system what “Hospitality” jobs were available in your area, the idea was to allow people to tell the service what skills they have, and to have relevant roles suggested back. This way, search results would no longer be constrained to the roles or sectors the jobseeker was familiar with. We also wanted to experiment with the idea of returning generic job titles, rather than specific roles, to see if this could be a useful pre-search tool for job seekers to identify new roles or sectors before using more traditional job search tools.
The underlying data science going on here is much better explained by Jeremy in his post on the project, but at a high level we analysed thousands of job specifications to build a model which clustered job roles according to their skills. The job roles are returned according to how closely the skills required match the skills entered.
The approach has a number of benefits over a free text search, including:
- Free text search will only return roles whose description uses the same words as have been entered into the search. Because our approach has learnt from many different descriptions of a role it means we know that a role which asks for a “team player” is likely to be suitable for someone who describes themselves as “collaborative”.
- We can reduce the impact of gendered terms when either used in job descriptions or as inputted skills. The model understands the similarity between skills and therefore it’s possible to automatically add non gendered (or oppositely gendered) skills to any input to increase the breadth of roles returned (this isn’t yet implemented in the machine).
- The underlying model provides a navigable graph which could allow jobseekers to browse roles by skills in order to help them explore further outside of their normal comfort zone. For example, you could imagine for any roles presented back to the user, there could be another 5 roles which require 1 additional skill. Clicking on one of these would open up another 5 roles and so on.
The skills gaps problem (b) is something we didn’t have time to implement in the proof of concept, but the approach supports it. For example, it would be relatively easy to identify the top roles that require 1,2 or 3 more skills in order to match the user’s input, and then display those jobs in the results along with highlighted skills gaps.
The PoC has demonstrated that a relatively simple dataset can produce a data graph which provides a powerful, intuitive way to find jobs, while also opening up the opportunity to develop innovative new capabilities like the gender-bias-reducer or graph browsing.
If you’re interested in discussing how the work done here might support challenges you’re facing with recruitment, then please get in touch, or you can contact the Emergent Alliance through the feedback form on this page.
About: The Emergent Alliance was set up to aid post Covid Recovery through developing data-driven insight and tools to be made available to businesses and government. DataSparQ contributed engineering, data science and product management effort towards the Regional Re-skill and Redeployment challenge submitted by Reed.
