Data science is one of the fastest-growing areas in the IT sector. Data scientists possess the technical know-how to manage complex problems and the curiosity to discover unanswered questions.
That’s why it has become one of the most popular professions in the market. And it’s no wonder that Harvard Business Review declared “data scientist” the sexiest job of the 21st century.
A recent study by McKinsey predicts there will be 250,000 vacant data scientist roles in less than a decade. That means you’re going to hire data scientists at some point if you haven’t already.
While the demand for data scientists is multiplying. Most businesses don’t have a clear hiring strategy to find and recruit data scientists.
The competition to recruit qualified data scientists from a vast pool of candidates is fierce. You also need to make sure your hire meets your organizational data needs and culture. Hiring qualified data scientists may sound tedious, but it’s not.
So rather than approaching hiring tasks with a traditional mindset, you must adopt new hiring strategies to recruit data scientists.
To recruit data scientists, you need to be a 100% clear about your organizational needs. This can vary from filling an open position, better managing a team’s workload, or expanding the reach of organizational tasks.
Factors like customer satisfaction, financial benefits, and reputation of a company depend mainly on its employees and their skills. For this reason, the hiring team must identify recruitment needs as the initial step.
Taking time to identify the real hiring needs and examining them now and then is an essential part of the process. Before starting your recruitment process, keep all the goals, facts, and challenges in mind.
A job description should reflect the responsibilities of a data scientist position. A well-written description will produce a realistic picture of the job. It also answers the question, “What does the person in this role do?”
Next, make sure to use language that is engaging and exciting. It would be best to use simple language to engage the reader, like using ‘you’ instead of ‘the ideal candidate.’
After all, if you don’t believe in the role you are selling, how can you expect a candidate to?
Here is what you can include in a data scientist job description:
A staggering 97% of data scientists currently feel burnt out, and 70% plan to leave in the next year.
Hence showcasing your company's work-life balance can be effective in recruiting data scientists. You can also mention the salary range your company is willing to offer for the data scientist job role.
Bringing the right hires on board is one of the toughest challenges for any business. With so many hiring options available in the modern workforce, knowing which path will be more beneficial can take time and effort.
So, what options are the best fit for your organization? Let’s dive in.
I) Hiring local talent: Hiring local full-time talent has stayed in the employment model for a long time. As digital technologies are emerging, offering new alternatives, some companies continue to prefer local talent for notable reasons.
II) Leveraging remote teams: With countless studies confirming that remote jobs accelerate business growth and employee retention, the benefits of remote working for employers are undeniable. However, it is still crucial for you to determine the advantages and disadvantages of whether a remote team is right for your business.
Now that you have set the ultimate goal of data science within your organization, every recruiter needs to look at specific skills and capabilities that are important for data scientists to have.
Some of the most important technical data scientist skills are:
Non-technical skills required for data scientists are:
Once you have identified a data scientist’s skills and requirements, it’s time to fill your recruitment funnel with qualified candidates. Here’s how:
Lack of candidate engagement, outdated LinkedIn profiles, limited talent pools — if these challenges sound familiar to you, it might be worth checking out some creative sources for data science talent.
For example, you can search techies in their natural habitat on sites like GitHub, Stack Overflow, and other popular gathering spots.
Big tech players like Google, Microsoft, and Meta are slowing or freezing hiring and even laying off staff. For growing companies, it’s a great time to take advantage and recruit data scientists — who might ordinarily be taken over by larger companies.
AI sourcing tools are perfect for recruiters that aren’t making any traction in their sourcing efforts.
These sourcing tools tap into vast talent networks of candidates from public sources.
It analyses a candidate’s:
These tools are becoming popular in the recruitment world as they find the best matches for your job description — even for specialized data science roles.
Check out our list of 12 Best Sourcing Tools for Recruiters in 2024
Once you clearly understand the ideal data scientist profile, you’ll be able to identify the skills a candidate will need. The next step involves crafting your interview process to identify and assess these skills.
For companies that don’t have the necessary knowledge of how to assess the capabilities of data scientists, it is essential to consult with organizations that are apt at assessing these specialized skills.
Like a skills assessment platform!
Why?
According to data, candidates feel that technical interviews are unstructured and don’t measure the skills necessary to succeed in a job. Most companies that recruit data scientists don’t know how to assess data science skills properly. On the contrary, a skills assessment tool can help recruiters objectively evaluate candidates quickly and at scale.
One specific tool that will enable you to measure a candidate’s ability to perform on the job is iMocha. iMocha analyses whether a candidate can tackle real-world tasks, and closely simulate what they will be expected to do if you hire them at your company.
Additionally, iMocha’s technical tests don’t trip up candidates; instead, it encourages them to show off their skills and knowledge.
Your goal as a recruiter is to find the best candidates and streamline the hiring process. This can be an exciting yet a challenging job. But what happens when you realize you cannot find qualified employees for the job?
Here are a few of the biggest challenges you might face as a recruiter:
With so many variables to cater to during recruitment, there’s no surprise that there are several challenges. The workforce is fluid and constantly changing due to technological advances.
As a recruiter, you must stay current on these changes and be prepared to face recruitment challenges. Utilizing technology to enhance your recruiting strategy is a must. And with the help of the right tools in place, hiring can be much smoother and more efficient.
With that in mind, we hope this blog will help you overcome challenges and recruit data scientists by developing an effective recruiting strategy.
Hiring the right candidate can be a challenge, but that doesn’t mean that it can’t be simplified. Remember that it is impossible to build an effective data scientist team without the right team members. So, make sure you are revising and adapting your hiring strategies.
A proper skills assessment tool will enable you to make a data-driven approach that moves past the traditional hiring process. These tests will help you recruit data scientists by assessing their hard/soft skills and cultural fitness, and in turn, drive better results for your organization.
How much does it cost to recruit data scientists?
According to the U.S. Bureau of Labor Statistics, the average salary for a data scientist working in the U.S. is about $195,000.
What is the strategy to recruit data scientists?
The process of recruitment is an integral part of running any business. The most successful strategy to recruit data scientists depends on your organization’s unique needs and the candidate you’re looking to hire.
Here are the top recruiting strategies to attract and hire quality candidates: