Now, Artificial Intelligence, big data will help you land a job

Updated - January 13, 2018 at 12:07 AM.

Start-ups offer solutions aiding workforce optimisation, talent transformation

Arjun Pratap_CEO and founder, EdGE Networks

For years, recruitment teams — hundreds of people dedicated to sourcing alone — have been trying to solve the ‘quality-of-hire’ problem for large enterprises.

Heavy dependence on external recruitment consultants have also added to hiring costs.

Yet, there’s been little to show that India’s technology enterprises, for example, had sure-fire solutions to their talent-related dilemmas.

Artificial intelligence and data analytics are transforming corporate human resource management (HRM) in ways recruitment portals and consultants cannot.

Employability factor

Big-data analytics company iPredictt Data Labs launched its SaaS (Software as a Solution) offering, Careerletics, last quarter “to exponentially improve quality of new hires at added speed”.

The platform can reportedly calculate an ‘employability score’ (checks if a resume matches the requirement) and the probability of offer declines by candidates. iPredictt founder and CEO Rohit Verma envisions Careerletics also generating a ‘fraud risk score’ shortly.

On the scope of Careerletics as it evolves, Verma says: “The platform will provide course recommendations to job-seeking candidates, earning (us) a commission from course providers. Individuals will be able to take assessment tests based on job roles; we’ll charge the test-takers for this. There’s also a huge market for employee verification, with companies demanding pre-verified resumes.”

Automated sourcing

That there’s been a ready market for automated sourcing becomes clear on encountering EdGE Networks.

The venture began building its application for automated sourcing (reportedly in 2012) to find an early believer in Wipro.

According to CEO and founder Arjun Pratap, the firm was formally signed on by Wipro in October 2013, and was fortunate to receive funding from the National Skill Development Corporation in July 2013.

Pratap says: “(If an enterprise gets) 40,000 resumes a month… (our solution) would read resumes and appreciate rules like ‘do not poach’ or the ‘do not hire’ blacklist, and then score and rank resumes based on skills competencies, abilities and traits.”

Talent optimisation

With the current US administration planning to squeeze H-1B visa quotas, and automation forcing lay-offs across enterprises, workforce optimisation and talent transformation has become pertinent to India’s corporations.

When an enterprise is better able to reallocate ‘bench’ or billable resources and restructure teams in a way that allows employees to be retained, significant ‘billing leakage’ is avoided, as Pratap puts it. Nearly 80 per cent of his business with Wipro is centred on workforce optimisation.

EdGE is also helping bring HCL’s career site up to speed for enhanced user experience and to enable a five-hour turnaround of suitable candidates for the company’s vacancies.

Unique arena

In 2016, recruitment start-up Hiree reportedly laid off a majority of its staff before being absorbed by Quikr to create QuikrJobs.

In the short to medium term, Quikr’s classifieds model will likely have its takers because of its diversified focus on blue-, grey- and white-collar jobs.

But if the classifieds route competes with sites such as Monster and Naukri, rather than keep up with technology innovations, will any of them survive the onslaught of machine intelligence in the longer term?

EdGE, claiming to have “gone through 22 million resumes and close to about 4 million job descriptions” by the start of this year, says it currently serves five large enterprise clients while attracting interest from the US market, too.

About 20 enterprises are currently in different stages of the sales cycle for Careerletics, according to iPredictt’s Verma.

The platform will also soon sport a consumer-facing side that replaces traditional models of career counselling, he said.

Published on February 15, 2017 16:11