How has the demand for Data Science changed after covid?

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The covid 19 pandemics surely has hurt the world economy and that has led to a decrease in employment rates. So much has been the decline that many economies have gone into a recession. As organizations try to grapple with the slowdown, job layoffs have been common. Among all this, data science has been one field that has managed to stay in demand.

The dynamics of its demand surely have changed. That doesn't change the fact that the demand for analytics and data science has been progressively on a high since the last several years. This 2017 report from IBM shows how data science positions would increase exponentially from 364,000 to 2,729,000 by 2020.

The demand has risen by 56% in just the first quarter of 2020. One reason behind that was because of the increase in industry's desire to make use of available data. The reliability of data sources and the effectiveness of analysis are other factors behind increased demand. The easy availability of technology also has to do with it.

Fierce Competition

Your new data science job doesn't need to be in your current city. Remote working and virtual hiring have allowed you to explore new avenues in data science and working with multinationals outside your geographical region.

That naturally leads to an increase in competition and you are suddenly competing with a better experience and skilled people from a different region. That particularly affects fresher who are introduced to more competition.

Conversely, you can even apply for jobs outside India and earn a better salary. The average salary of Indian data scientists is way lesser than those in the US and you can bridge the gap now.

The need of the hour hence is to keep upskilling and looking for new and better opportunities even if they are remote. To support your dreams, companies like Business My Toys have launched very interactive and advanced courses in analytics and AI to ensure you don't miss out on any opportunities with the greater competition and always stay ahead of the curve.

Upskilling is now on you

Upskilling is an essential aspect of a dynamic and evolving field like data science. The demand for data scientists with multiple skills has increased as more and more data scientists are looking for the same job post-pandemic.

Layoffs are one reason why companies have more to choose from and you would be fighting with upskilled employees who have gained various certificates even before applying for the job. A LinkedIn survey reports that about 64% of employees have upskilled this lockdown.

And the whole learning process is on you now because the companies aren't offering training programs and in-person workshops anymore now. You won't get accustomed to the workplace as easily and smoothly from a remote location and hence learning those skills online can help.

You should ideally go for a course to gain knowledge and then keep applying them to various scenarios.

Contract-Based Hiring on the Rise

Contract-based hiring has increased post covid because businesses are now relying on cost-cutting measures. The hiring of freelancers has increased and so has hiring of contract-based workers. If companies can't higher highly pay data scientists and analysts, they would be able to hire them for projects on a contractual basis.

The trend would continue until the workflow starts to get back and the economy paces again. Contractual hiring surely is quite beneficial for companies. They pay only for they utilise here unlike having salaried employees whom you have to pay even when there is a shortage of work.

The trend has transcended to data science and analytics jobs. Perhaps some companies still perceive these jobs to be secondary and hence want to save money from cutting the budget of this department.

That has brought a huge challenge to data scientists who are looking for secure full-time jobs as they are getting freelance offers. Contract-based workers come with the benefit of flexibility and cut on costs for the companies.

Moreover, businesses are looking to hire more multiskilled employees and generalists. Instead of having domain-specific knowledge, you should concentrate on getting the knowledge of integrated data science as a whole. Being a full-stack data scientist at this point can boost your chances of getting a job.

Factors that affect Opportunities in Data science post covid

1. Return on Investment

Return of Investment on data science deeply influences the investment in analytics and data science. While the investment has been seen as a long term one that would pay rich dividends, the reality is a bit different.

Many algorithms never even get deployed into product applications. A lot of big data projects typically fail and the calculating ROI has typically been challenging. It's been very subjective but companies also want a measure of this.

Although the investment is constantly increasing on analytics, the same can't be said about ROI. The groups that have focused on predictive and descriptive analytics don't have a lot of deployments leading to their downfall.

Those companies may cut on data analytics jobs in these times of recession and slowdown.

2. Analytical Leadership

The companies whose higher executives believe that their company is data-driven would surely continue to invest more and more in data analytics. But if the C-level support for a data-driven culture isn't there, companies might withdraw their investment on a cut on analytics costs.

If a company's close to realizing value from its data, it's likely to continue investment in AI and data science. The company in such a case typically progresses from descriptive statistics to predictive analytics.

The demand for prescriptive analytics has increased during the pandemic since optimization is being applied to human resource management and logistics. Analytically mature organisations, as a result, would demand an increase in data science services.