Leaders have come to realize that data-driven decision-making adds great value to their business. Hence, the demand for data professionals has skyrocketed across a wide range of industries. On the flip side, it is also getting difficult for recruiters when it comes to hiring data science teams and roles where the talent is scarce.
In one of our earlier write-ups, we addressed the demand and supply of data science roles and talent, respectively, across industries. We concluded that bridging the demand-supply gap for data science roles is a distant dream unless industries are done chasing shiny job roles such as data scientists, data architects, and data engineers. However, a few new-age niche roles exist, such as data ethicists, behavioral psychologists, and data storytellers, that add immense value to teams and projects.
Check out how to bridge the demand-supply gap for data science talent
Recruitment challenges while hiring data science teams
Now, let’s talk about the challenges that industries face while hiring roles for a data science team. Evaluating tons of candidates and picking a game-changing few is not an easy task. Multiple challenges come in the way.
1. Resume Padding
Anyone who is responsible for hiring good talent for the organization must have a keen eye for resume padding. Candidates exaggerate the information in their resume to improve their credibility for the job. Almost three out of 10 screenings conducted by HireRight in India from January 2014 contained a lie or a discrepancy.
2. Salary Expectations
Tech Giants are doling out huge salaries for scarce AI and ML talent. Without a doubt, there is a shortage of talent, and the big companies are trying to land as much of it as they can. Thus, the talent pool available in the market dries up for smaller companies and startups.
3. Job Hoppers
Due to heavy salary expectations, job-hopping becomes common. People switch companies when they get a better offer, which is commonly from a small company to a big one. Research from Burch Works found that 17.6% of data scientists and analytics professionals changed jobs in 2018, with an average tenure of 2.6 years.
4. Hiring for Geography
Attracting talent to the geography where your business or industry is thriving is another challenge. For instance, if a company wants to set up a data science team for a shale oil production unit in a remote location in the Balkans.
Not many data science talents may be open to moving to such locations or industries, especially because they might get job offers in better locations. Such specific requirements will need companies to set much higher salaries.
5. Industry
In one of the analyses we did earlier based on a month’s data science job postings on LinkedIn, we found that the maximum talent is hired by the computer software industry. However, industries such as pharma, financial services, and automotive are working closely with data professionals to innovate solutions. This is a challenge for recruiters right now. But, we can see other industries caught up in the race pretty soon.
6. Lack of soft skills and domain skills
It is true that the soft skills of data experts will make or break their value to the team. The ability to communicate business insights simply is the key to long-term success in the field of data science. The ability to create consumable dashboards and progress reports to translate findings into business solutions is a requisite skill for data science talent.
7. Justifying full-time hiring
According to the analysis of LinkedIn data science jobs, out of 55,469 job posts, 50,722 jobs were rolled out for full-time positions. This clearly shows that organizations look for long-term commitment and engagement. However, there can be instances where companies may not need a dedicated full-time data science team (due to budget constraints, lack of projects, or small projects).
Tips to solve the challenges of hiring data science teams
The growing demand and limited supply of data science experts, has created a shortage of talent. Here are three pieces of the puzzle that your organization needs to unlock to overcome the challenges of hiring a data science team.
1. Reducing the time taken to hire
Hiring talent at the right time makes sense when your projects are about to start. If the project is midway or at its peak, or the need has already been met, it would be difficult to justify the hiring at a huge cost.
2. Offset the talent shortage
This challenge is to get the data science talent to come on-board and convincing them to join the organization. The first (but by no means the only) requirement is to meet the salary expectations for the role and the location. You can also take a series of steps like being explicit about the work they will be doing, career progression and flexibility offered and having constant follow-ups and exhibiting interest about them coming on-board.
3. Accelerate business transformation
Isn’t this the ultimate goal of hiring a data science team? The above two points contribute to the third one. It would be good to have regular meetings with the business executive of the company to create strategies to unlock the full potential of a data science team
Watch the full webinar to know more about the importance, challenges, and solutions to hiring data science teams
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