Why Data Science Jobs Market Is Better Positioned For Recession

The ongoing COVID – 19 Pandemic is adversely impacting the global economy, with expected record unemployment, business disruption, and significant contraction of global GDP growth.

While data science and analytics jobs are not immune to recession and economic woes, there are multiple cases of hiring for data science roles in various companies. Here is one.

The data science job market has also seen fewer lay-offs, salary cuts and furloughs compared to other tech job roles. This is also evident from our own tracking of the lay-offs in the tech domain of India. 

If you look at the laid-off staff, it is focused on IT roles and positions which can be fairly replaced with automation tools. Data science roles, on the other hand, are incredibly challenging to automate at this point in time. So, job cuts in data science may be minimal if you look at companies removing redundant roles in the workforce.

It has been found in research that job volatility in data science has been on the lower side compared to traditional jobs that have been disrupted due to supply chain issues, according to a report. 

In the report, jobs such as teachers, manufacturing, and hospitality have been witnessing the highest volatility in the ongoing crisis. Software and IT jobs have been found to have mid-level of volatility compared to more traditional jobs. 

However, different sectors have different conditions -- including types of businesses, geographies, and consumers served, debt load, capital and operation costs, and supply and demand for human resources. So, the impact is going to be different in each case, be it the IT sector or FMCG, Industrials, or Domestic Banking. 

And if we look at how Indian IT industries have performed in the last five years, the tech sector is expected to stay strong, barring some overvalued startups and legacy IT companies. Specialised data science and analytics consulting firms are hiring despite the recession. On the other hand, we have seen lay-offs in startups, IT and services firms. 

What Makes Data Science Jobs Safer During The Recession

Analytics is going to be very crucial in the times of recession for optimising costs and reducing wastage of resources. Quoting IDC’s numbers, WSJ reported that 2020 could see rising demand in AI jobs, given AI applications are being deployed extensively by healthcare providers, educational institutions and government agencies. According to the report, overall AI spending will also increase in 2020. Here, data scientists would play a critical role in getting the best of automated systems that throw out chunks of data.

The findings from the AIM survey reveal that the entire analytics community is able to work from home. Moreover, while analytics personnel are working from home, they have access to the required tools and platforms to execute their tasks and responsibilities. Moreover, most of the respondents have either experienced no impact on their work or have experienced a positive effect on their job because of higher productivity. The broad data science domain makes working from home possible under certain conditions, including the type of projects, variety of functions, access to tools, employee engagement, and overall connectivity and collaboration with the rest of the team/organisation.

Given data related jobs can be done with ease over the web and from home, and its growing importance in the tech sector, it has seen minimal volatility despite the slowdown. Regardless, data scientists could still lose their jobs, particularly in more vulnerable sectors such as travel and tourism, logistics, and even retail. 

Overview

2019 was a great year for the data science jobs market, and the trend is likely to continue despite the global pandemic. In fact, given everything is being virtualised and platformised, in some industries, we are witnessing the advent of more data and more extensive data workloads. This will fuel the demand for more data scientists in such fields. 

And if firms do lay-off their analytics and data science teams, it would be even a bigger challenge to rebuild those teams. Getting an efficient data science team takes years to build, and it would be a waste of millions of dollars to let-off valuable staff.