Strategy to Recruit Data Scientists in 2023

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.

• Industry experts estimate that 2.3 trillion gigabytes of big data are created daily.
• Studies by IBM found that they generated 90 percent of the world’s collective data in the last two years alone.

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.

How to develop a recruiting strategy for data scientists

1. Define the needs of the company:

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.

2. Create a job description:

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:

• Job title
• Overview
• Company introduction
• List of responsibilities
• Daily task
• Required qualifications and skills

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.

3. Choose between local and remote talent:

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.

• Pros: Easier to train in-house, more straightforward communication, collaborative environment, etc.
• Cons: Shortage of specific skill sets, more significant overheads, and time-consuming processes.

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.

• Pros: Larger talent pool, scalability, efficient processes, cost-effective, etc.

4. Define the critical skills and qualifications required:

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:

• Statistical analysis and computing
• Machine Learning
• Deep Learning
• Processing large data sets
• Data Visualization
• Data Wrangling
• Mathematics
• Programming (R, Python, Scala, JavaScript, SQL, Spark, C, and C++)
• Statistics
• Big Data

Non-technical skills required for data scientists are:

• Communication skills
• Data intuition
• Problem-solving
  

How to source data scientists

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:

1. Consider data science talent pools:

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.

2. Take advantage of the talent from top data companies:

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.

3. AI sourcing tools can help you find the best matches:

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:

• Online presence in technical communities
• Professional and social networks
• Company websites
• and Personal websites

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 2023

How to assess the skills of the candidates?

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.

Here are few approaches you can take to assess the skills of a candidate with iMocha:

• iMocha’s offers the world’s most extensive skills test library with over 2500+ skills, including coding, next-gen, app development, communication, etc.
  
• This AI-powered tool offers skill-based talent analytics to help you make more informed recruiting decisions.
• Take-home assignment: This is an in-depth coding task, which allows the candidate to use a comprehensive IDE and complete it under a more flexible schedule. Once completed, hiring managers can review their assignments and provide feedback.
• Live remote coding: It enables you to observe a candidate’s programming skills in real-time from anywhere. The candidate needs to solve a coding challenge using a simulated coding environment, which they should aim to complete in a specific time frame.
• AI-LogicBox: An innovative pseudo coding platform to assess a programmer’s approach without having to write the entire code from scratch.

Challenges you might face while recruiting

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:

1. A vast pool of candidates without the right qualifications
2. Losing candidates to competitors
3. Providing an insignificant interview experience
4. Delayed hiring process
5. Building an employer brand
6. Overcoming recruitment biases

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.

Conclusion

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.

FAQ Reference

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:

1. Develop an employer brand
2. Job posts that reflect your company culture
3. Invest in an applicant tracking system
4. Explore niche job boards
5. Pay to boost job listings
6. Revisit old resumes
7. Consider campus recruiting
8. Engage passive candidates
9. Conduct practical skills assessments