Job Prospects for Data Scientists in the Post-Pandemic World
The job outlook for data scientists remains positive despite a year of challenges.
Indeed’s Hiring Lab found that tech job postings were negatively impacted by the pandemic. In addition to the decline, the tech sector has seen a lackluster recovery.
But the job prospects for data scientists remain bright in a post-pandemic world. There are signs of a strong recovery and an increase in the value of data science jobs and the overall technology labor market.
According to Glassdoor’s 50 Best Jobs in America for 2020, data scientist remains one of the top three jobs in the United States, with high job satisfaction and a median base salary above $100,000. And despite a drop in job postings, becoming a data scientist remains as desirable as it was at pre-pandemic levels.
Data scientists continue to grow
The dramatic decline in job vacancies does not paint the whole picture. The value of data literacy and analytical ability has not diminished, but rather the ability of organizations to hire these high-value positions has diminished.
“There is no indication that they will be in less demand,” said Frank Buytendijk, an analyst at Gartner. “The number of chief data officers continues to grow.”
When the pandemic started, many organizations froze hiring when the length of the shutdowns was unknown. According to Nancy Darian, data science and analytics recruiter at Smith Hanley Associates, hiring of data scientists plummeted with the 2020 recession. The need for data scientists has not diminished, but new hires for this position are just beginning to make themselves felt. “I would say we are coming out of the depths of the pandemic crisis,” she said.
In February, hiring began to pick up with slight increases every month since, according to Darian. And even when the hiring of data scientists was low, the need was still high.
“Companies made the most of those they already had on staff, but there weren’t a lot of hiring going on,” she said.
The need for data scientists
Organizations are still relatively data illiterate and data analytics is still a relatively young industry. As data science and analytics continue to grow, it is important to acquire and retain experienced and skilled employees.
“Data science in a business context goes beyond just research and statistics with the data provided to you, as it requires the person to understand the entire data pipeline,” said Steve Tycast, Director data and analytics at AIM Consulting Group.
In his observations at Gartner, Buytendijk found that job postings were increasingly adding analytical requirements. Successful organizations seek to combine skills for data analysis and management. And companies ideally hope that this combination can come from a small core.
Darian said that over the past few years, data science has emerged as a candidate market. The hype around a shortage of data scientists has led to a popularity of data science bootcamps and college offerings. But regardless of the large pool of applicants, data science job prospects are best for the most qualified and experienced data scientists.
“There are so many graduate trainees and programs out there, but it’s hard to find really good, experienced people,” Darian said.
Data scientists and data science teams
The pandemic has forced organizations to rethink hiring data scientists. Instead of dedicating significant funds to an individual, organizations have sought to build better data science teams.
Companies look for a set of skills when building their data science team. There must be people with business skills and people who can understand what your data can be used for and how it can improve business operations. To support them, you need team members with quantitative skills, i.e. able to understand the models and their limitations. There must also be people on the team with certain technology skills who are able to handle data management.
“You bring together people with different skills and strengths to create the entity,” Buytendijk said. “And the odds of one person being expert in all three are pretty low, so you’re trying to put together small teams of people who have cross-skills.”
For organizations unable or reluctant to commit resources to data science positions, there is a need to turn to multiple hires to solve the overarching problem of collecting, processing and applying data through analytics and machine learning.
Data scientists are often meant to be the combination of all three and are hard to find and keep. Their combination of skills and business reliance on data is linked to their increased popularity and demand.
“An exceptional candidate has the ability to think critically, demonstrating a deep understanding of the business problem and the ability to walk me through the steps of creating the solution,” Tycast said. “It’s not just technically delivering a result, but showing successes and failures using various variables and algorithms to get the best answer.”
Continuous learning is necessary for a positive data scientist job outlook at the individual level. Darian said enrollment in bootcamp and Coursera courses has trended over the past few years as the skills needed have evolved.
“There’s definitely been a big upturn in those kinds of classes,” she said.
Although data science job prospects are strong, it can be difficult for organizations to keep such in-demand employees. Salaries are competitive and demand is high, so it’s more important than ever that your data scientists and analysts have an incentive to stay.
“Employers want to be competitive, but it’s not like ‘the sky’s the limit,'” Darian said. “[Data scientists] are well paid and they expect to be well paid. »
Buytendijk saw two effective strategies for retention in data scientist jobs. The first is to provide a lot of training to employees. Data scientists have a natural curiosity and a strong desire to keep learning.
Providing continuous learning opportunities is important, especially if the position is not inherently difficult for the employee. Buytendijk also said giving data scientists more responsibility and the ability to interact with business executives helps with retention.
This is a sign of the times in the field of data science. Integration and interaction between the data analytics part of the organization and senior management has become a popular trend as the business reliance on data has increased. And to reflect this dynamic change, organizations need to keep both pillars abreast of each other’s operations. Data scientists have become an important part of the data-driven business and providing these employees with reasonable accountability and C-level communication only makes sense.