There is an inherent conflict at the point of basic information acquisition in the process of hiring. The question is how much information should the candidate be required to fill up while uploading his resume? Too much information increases his/her work. On the other hand if minimal information is acquired ; hiring managers are left with a whole lot of resumes and very little information. Its frustrating for hiring managers to read a number of unsuitable resumes before getting one that is suitable.
To elaborate this situation let me take the example of my company. We were getting hardly any interesting resumes from our website. We decided to do away with the lengthy process and made it very simple. Now we have an apply button in front of each opening on the careers page; all that a candidate needs to do is to upload his latest resume. But simplifying this process resulted in a whole lot of resumes being uploaded. Now our HR executives are spending significant amount of their time managing resumes. Our hiring funnel in the chart below shows more than 99% of resumes being filtered out to make less than 1% offers.
Should we switch back to our old “elaborate” process? Will the “elaborate” process and form filling ensure that hiring managers get what they want? The reality as we learnt from our experience is quite the opposite. Really interesting candidates don’t bother to go through the ordeal of “registering” and uploading their resumes . And those who do are not really interesting and those who appear to be interesting are just that. They “hype up” their resumes to make themselves appear interesting.
I had this problem on my mind when I attended a day long event focused on applications of machine learning
Of all the talks I was most inspired by this talk by Nilesh Phadke of BMC Software. He demonstrated applications developed for the IT support – far away from the world of hiring. However I felt that the problem I had on my mind could be solved by applying the machine learning approach.
Information Extraction for Filling up Forms
To automate any workflow ; one needs to enter long forms about entities – be it a support ticket or new candidate for a company. Long forms demotivate users and introduce an element of delay. There also is a tendency to skip non-mandatory fields even though the information is available.
Nilesh demonstrated “Formless Incident Creation” where the user was allowed to type a complaint in one Text Field. As he filled in details, based on the words that he was typing the fuzzy matching algorithm in real time matched the correct entities to those words. Not only did it complete the form needed but also searched and found similar past incidents from a myriad of templates of typical incidents.
Machine Learning Approach to Catch-22 of Hiring
I was immediately reminded of the catch-22 situation in hiring. Most of the information is already present in the resume that is being submitted. Can we not use information extraction algorithm to automate this tedious task? Can we also have different templates for Developers, QA Engineers, IT Support Engineers and Project Managers? Can we use the information extracted to synthesize micro-resumes or short summaries of less than 500 characters to help hiring managers quickly read resumes on their mobile devices?
This gave a new direction of thinking to resolve the catch-22 situation. Here are the set of tools that we plan to use.
Fuzzy Matching – Solr
Natural Language Processing – OpenNLP
Analytics for unstructured text-UIMA
Please stay tuned for updates on how we use machine learning for formless resume acquisition and more efficient search by hiring managers.