Job prospects for a data scientist in the post-pandemic world

Job prospects for data scientists remain positive despite a year of challenges.

Indeed’s Hiring Lab has found that tech job postings are being negatively affected by the pandemic. In addition to the decline, the tech sector experienced 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 tech labor market as a whole.

According to Glassdoor’s 50 Best Jobs in America for 2020, the 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 decline in job openings, becoming a data scientist remains as desirable as it was at pre-pandemic levels.

Data scientists still growing

The dramatic drop in job vacancies does not paint the big picture. The value of data mastery and analytical ability has not diminished, but rather the ability of organizations to hire these high value-added positions.

“There is no sign that they will be in less demand,” said Frank Buytendijk, analyst at Gartner. “The number of data managers is always increasing. “

When the pandemic began, many organizations froze hiring as the duration of the lockdowns was unknown. According to Nancy Darian, data science and analytics recruiter at Smith Hanley Associates, hiring of data scientists has plunged with the 2020 recession. The need for data scientists has not abated, but new hires for this role are coming all the way. just getting started. “I would say we are emerging from the depths of the pandemic crisis,” she said.

In February, hiring started to pick up with small increases every month since, according to Darian. And even when the hiring of data scientists was low, the need was still high.

“The companies made the most of those they already had on their staff, but there weren’t a lot of hires going on,” she said.

The need for data scientists

Organizations are still relatively illiterate when it comes to data, and data analytics remains a relatively young industry. As the science and analysis of data continues to grow, it is important to acquire and retain experienced and knowledgeable employees.

Data science skills are evolving, but these are some of the main ones you need.

“Data science in a business context goes beyond just research and statistics with the data provided to you, because it requires the person to understand the entire data pipeline,” said Steve Tycast, director of data and analytics at AIM Consulting Group.

In his observations at Gartner, Buytendijk found that job postings added more and more analytical requirements. Successful organizations seek to combine skills for analysis and data management. And companies ideally hope that this combination can come from a small nucleus.

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 popularity in data science bootcamps and academic offerings. But regardless of the large pool of applicants, data science job prospects are better for more highly skilled and experienced data scientists.

“There are so many interns and graduate programs out there, however, it’s hard to find experienced and really good people,” Darian said.

Data scientists and data science teams

The pandemic has forced organizations to rethink hiring data scientists. Instead of devoting large amounts of funds to an individual, organizations have sought to build better data science teams.

Companies look for a skill set when building their data science team. There should be people with business skills who can understand what your data can be used for and how it can improve business operations. To accompany them, you need team members with quantitative skills – those who can understand the patterns and their limitations. There should also be people on the team with certain technological skills who can handle data management.

“You bring together people with different skills and strengths to create the entity,” said Buytendijk. “And the chances of one person being an 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 unwilling to devote resources to data science positions, it is necessary to resort to multiple hires to solve the overall problem of collecting, managing and applying data through analytics and machine learning.

Data scientists are often thought to be the combination of the three and are difficult to find and keep. The combination of their skills and companies’ reliance on data is linked to their increased popularity and demand.

“An exceptional candidate has the ability to think critically, demonstrating that they fully understand the business problem and have the ability to walk me through the steps of creating the solution,” Tycast said. “It’s not just about delivering a result technically, but showing successes and failures using various variables and algorithms to get the best answer.”

Continuous learning is necessary for positive job prospects as a data scientist at the individual level. Darian said enrollment in bootcamp and Coursera courses has evolved over the past few years as the skills required have evolved.

“There was definitely a big pick-up in this kind of course,” she said.

Retention

While job prospects in data science are strong, it can be difficult for organizations to keep employees in such high demand. Salaries are competitive and demand is high, so it’s more important than ever that your data scientists and analysts are encouraged to stay.

“Employers want to be competitive, but it’s not the sky’s the limit,” said Darian. “[Data scientists] are well paid, and they expect to be paid well. “

Buytendijk saw two effective strategies for retention in data scientist jobs. The first is to provide employees with a lot of training. Data scientists have a natural curiosity and a strong desire to keep learning.

It is important to provide opportunities for lifelong learning, especially if the position is not inherently difficult for the employee. Buytendijk also said giving more responsibility to data scientists and the ability to interact with business leaders helps with retention.

It’s a sign of the times in data science. The incorporation and interaction between the data analytics portion of the organization and senior management has become a popular trend as companies’ reliance on data has grown. And to reflect this dynamic change, organizations must keep the two 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 just doesn’t make sense.

Sean N. Ayres