Bengaluru: A move toward project-based work is prompting Indian information technology (IT) services companies to adopt the flexi or contractual staff model while hiring professionals in high-tech areas, spelling rising costs for companies. According to the latest talent market research by Infosys, called Talent Radar 2019, the top five technical skills in demand in digital projects are: analytics, user experience, automation, IT architecture and artificial intelligence. Companies like Tata Consultancy Services Ltd, Infosys Ltd, Wipro Ltd, HCL Technologies Ltd and Tech Mahindra Ltd have adopted this model in varying degrees. The sector's flexi workforce is expected to grow to 720,000 by 2021 from half a million in 2018, according to ISF. To be sure, the move toward contract hires has been a trend across industries, but it has become more visible in IT of late because of a rise in project-based work.
Newer technologies like AI, Machine learning or blockchain will together boost the IT staffing industry creating new jobs. According to ISF report on IT Staffing Industry Landscape, Karnataka, Maharashtra and Delhi NCR are the top 3 states that dominate the IT Flexi Staffing Industry in India. Karnataka dominates the IT Flexi Staffing market standing at $1.05 billion followed by Maharashtra at $0.48 billion and Delhi NCR at $0.23 billion. With the rise of technologies such as cloud computing, artificial intelligence, IoT, Big data and machine learning, the demand is growing for new technology-based services. Mobility and cloud computing each standing at 63 percent are in maximum demand followed by cybersecurity at 53 percent and analytics at 51 percent - they comprise the top technology drivers across organisations for their proficient functioning.
When I went on a break to take care of my children, I was in marketing. When I decided to come back, the work itself had changed to digital marketing," says Franky Aggarwal, a 40-year-old working mother in Pune. Aggarwal, after doing a one-year digital marketing certification course, is now working for a US-based personal care brand through FlexiBees, a platform that reemploys female professionals part-time or on a work-fromhome arrangement. Women are leaving work as young mothers or caregivers, resulting in a leaky talent pipeline across sectors. Even as the pool of second-career women -- those returning to work after a break -- is growing, the tech and digital disruption that is changing the way India Inc works is making it increasingly difficult for them to come back. In fact, technology-led disruption is the newest gender-diversity challenge in corporate India. Companies such as IBM, Microsoft and Ingersoll Rand are rolling out programmes to deal with this. In December 2018, the World Economic Forum's "The Global Gender Gap Report" noted that the increasing expansion of artificial intelligence was creating demand for a range of new skills, among them neural networks, deep learning, machine learning and tools. It said: "Only 22% AI professionals globally are female, compared to 78% who are male.
On three occasions this week, I asked a FlexiSpy salesperson a simple question: If I wanted to, could I use their spyware to snoop on my wife's cellphone without her knowing? The answer each time was yes. When asked if it was legal, they responded with a canned disclaimer explaining it was necessary to get the permission of the target. But what if I didn't want my wife to know? They could help me anyway.
New Delhi: Hiring based on machine learning is 20-25% more efficient than manual hiring, a survey by recruitment and staffing company TeamLease showed. The report'The New Landscape of Hiring', shared exclusively with Mint, says the time, cost and attrition rates in machine-based hiring are lower than in manual hiring. Machine learning-based hiring is a process in which recruiters can use algorithms powered by machine learning to hire candidates. "With machine hiring, one could estimate attrition (early/premature as well as long-term) likelihoods and therefore choose candidate types that are associated with lower likelihoods. This is a rare (or not a) possibility in case of manual hiring unless data is manually captured and analysed," said Rituparna Chakraborty, co-founder and executive vice-president, TeamLease.