Data Science Driven Recruitment

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Availability of data and information in recent times is changing the recruitment process massively, starting from sourcing to authenticating candidate credentials to final placements. When it comes to hiring talent to full fill key positions in various organizations from entry level to top level, it is very important to select the right candidate. Particularly for top level positions (Directors, Group Managers, Chief Executives) extra precaution need to be taken as these positions carry a lot of responsibility-they are partners in profit, hold significant amount of shares of the company and also act as torch bearers for the company. At the same time for mid-level or entry level positions, when an employee leaves the organization or it is discovered that the employee is not fit for the role that he was hired for, by that time company would have spent a bomb on training, compensation and reimbursements including the cost for back fill the position. The cost of a ‘bad hire’ to an organization is five times the bad hire’s annual salary and hence companies should focus on hiring the right talent to mitigate business risks (Times of India, May 25, 2015). There are three major challenges that the recruitment industry is currently facing:

  • Pressure and urgency to fill up open positions in different companies, having a variety   of open positions
  • Higher number of applications every year, fresher’s and competitors applying for same positions
  • Lack of proper reference checks and background verification

In order to restrict the wasteful expenses and non-value added activities and minimize business risk from recruitment process, can Analytics, Data Science, Machine Learning and Artificial Intelligence be used to make a recruiter a Smart Recruiter? 

Let’s look at how data science can help a recruiter to excel the science driven recruitment process.

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    Sourcing and Matching:

    Access to digital profiles now days, is not a challenge, thanks to LinkedIn and other job sites where a pool of profiles reside. For a recruiter how to search the best fit from the pool is a real challenge. Can big data come to the rescue? Yes, unstructured data analysis along with application of natural language processing frameworks can establish similarity between a desired profile and the profiles available. One of the leading companies in recruitment space, CIEL HR recently adopted a product developed by Ma Foi Analytics for resume matching and subsequent scoring. The Resume Relevance Algorithm developed by Ma Foi Analytics parses the CVs to extract its context – skills & experience. Statistical procedures are applied on the derived context to rank the CVs against the context of the Project/ Assignment of the User Company and show top matches to the user. The product is believed to increase the fill rate from current industry benchmark of 20% to somewhere near 60%. Therefore the challenge now can be addressed. The product not only matches the best profiles from the pool, it also suggests top N matches based on key search criteria. As a result a significant amount of recruiter’s time that is spent on searching and matching is reduced and recruiter can spend that time on some other meaningful activity.

    • Evaluations:

    As the number of applications have been increasing at an exponential rate over the years, screening of candidates becomes very important. The first level of evaluation starts when a recruiter compares different profiles that are being selected by the algorithm. The second level of evaluation is required by interacting with the candidate. The big data analytics approach can solve the second level of evaluation as well. The following process explains the scientific way of evaluation.

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    The fake interviews e where there is no authentication that the person being interviewed over telephonic interview is the same person being hired given the above process, when the telephonic interview is recorded the same should be conveyed to the candidate before taking interview. This can reduce such instances drastically. The current industry practice of recruiter’s interaction with the candidate is either captured in notebooks or not captured at all. The entire conversation can be captured and converted to text, so that the same can be stored in structured data bases. In future the consistency of statements made by the candidates can be tracked in order to avoid debates and discussions. Based on the conversation sometimes the recruiter needs to take a call on whom to select, at that time the insights would be readily available in a summarized form, so that the recruiter does not have to recall what happened over the previous discussion.

    • Background verification:

    The pressure and urgency to fill up the open positions and higher number of applications, somehow gives room for the candidates to ‘game the system’. Though there are various background verification agencies that do it for big companies, there is a possibility of creating an alternative way to do this. Check prospective candidate’s LinkedIn profile to cross verify the information, send emailers to colleagues from past organization. Pull the social media posts, like twitter and Facebook posts and mine the text data to understand the candidate better. In the recruitment space, if the three problems as mentioned above can be controlled and improved by even a certain percentage adopting big data analytics and machine learning, a lot of money can be saved. Indian firms are estimated to have lost at least Rs 2,460 crore in bad hiring in 2012. The figures were Rs 2,270 crore in 2011 and Rs 2,120 crore in 2010, says a study on Bad Hiring Activity in India by recruitment tendering platform (MyHiringClub.com). The figure for 2013 is still date not available, but guess I can say it would be around 2000 crore plus. Introduction of big data, data science and machine learning methods and approaches as discussed above are only directional, there are a lot more possibilities given the data and features that are captured.  

    Blog Author: 
    Pradeepta Mishra
    Categories: 
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