Just like technology, jobs too, have evolved a lot from what they used to be. As a consequence, the hiring scenario has dramatically changed over the last two decades. One of the key parameter to have been impacted is the job duration. Companies are no longer limited to just having long-term employees. They now prefer to go “flexible” when it comes to choosing best hires for their job openings.

Flexible staffing or flexible hiring has become a commonplace term across employers as they are hiring people on contract and keeping them on a short-term project basis. In this article, we will look into how data science in India is slowly observing the flexible staffing fashion in hiring.

Is The Data Science Field Affected By It?

According to a report by the Indian Staffing Federation (ISF), almost 58 percent of Indian IT companies hire flexi-staff. This is a staggering number in this day and age. One reason for this is because companies tend to look for a broader set of skills in the tech industry. With attractive areas such as data science, machine learning and artificial intelligence applications already on the rise, tech jobs are expanding in terms of expertise.

Rituparna Chakraborty, President at ISF emphasises that organisations prefer flexi-staff mainly due to core expertise in niche technologies as well as new skill sets. In the report, which was published in May 2018, they covered key points such as automation and disruptive technologies in the IT sector which led to fewer job creations. As a consequence, flexi-staffing has become a popular hiring option instead of going for old-school permanent hiring.

When it comes to hiring data scientists, the flexi-staffing method is slowly catching up. For example, the number of freelance data scientists on LinkedIn is growing. Thanks to the site’s powerful networking advantage, it becomes easy for people to showcase their data science skills so that recruiters can see this and hire them on a flexible basis.

In addition, jobs posted online also cover flexi-staffing. If you search for a “data scientist” position on Indeed, you can see that for every 1,000 full-time jobs in India, there are at least 110 jobs that fall under contract, leased hiring, part-time jobs or commission-based jobs. On the other hand, even ‘remote’ data scientist jobs are also seeing a steep rise.

Working remotely has become a de facto trend in flexi-staffing. This is because it can increase productivity and decrease costs. A superb example is the time it takes to travel to work each day. In a city like Bengaluru where traffic is searingly getting worse, commuting can drain one’s focus on work and this indirectly affects productivity.

Sanjay Lakhotia of Noble House Consulting exemplifies this work culture in the country. He says, “While this [flexi-hiring] trend was first adopted by startups, multinationals and large enterprises alike have been quick to embrace it as well. About 44% of business leaders say that one of the top socio-economic drivers of change in business is the “changing nature of work, flexible work”. Estimates by the global research company IPSOS indicate that about 57% of Indian employees telecommute frequently and another one-third work remotely every day – thus doing away with the stress that accompanies long travel and working hours. This kind of work culture also boosts productivity by teaching people how to attain a higher level of achievement and enjoyment, both on and off the job.”

Will Flexi-Hiring Benefit Data Science?

In our theme of the month Analytics Hiring Scenario, we saw how human resource experts from major analytics companies gave out the nuances when it comes to hiring data scientists. Here, key discussions were on the skill sets required in data science and how it would fit into the business roles. Flexible staffing can alleviate this difficulty since people of different skill sets could be hired on a temporary basis to check whether data science is really compatible with this aspect. This way even businesses can reduce hiring costs and concentrate on narrowing with the actual requirements.

Conclusion

While flexi-staffing is largely beneficial, one of the key setbacks is employee-company connect. Hirees may sometimes feel they are distanced from activities happening in the workplace. Data scientists are not different in this aspect either. At times, it is necessary that they interact with their co-workers and sync with the details surrounding their data science project.

The prime reason employers consider flexi-hiring is mainly to minimise costs. Instead of paying lucrative salaries to data scientists, they can achieve their project with modest costs by flexi-hiring. But, this again, comes with a compromise on the quality of work as full-time are generally more conscious and dedicated to their work.