One of the most sought-after professions in the twenty-first century is that of a data scientist. Markets & Markets forecasts that the market for data science platforms, which was valued at USD 95.3 billion in 2021, will rise to 322.9 USD billion by 2026.
According to Firstround.com, hiring success rates are often around 50% in a competitive area like data science where exceptional candidates frequently receive three or more offers. The secret is to expedite potential employees’ hiring process so recruiters can fill data scientist roles more quickly. And this can only be achieved if the proper objective is set, much before you begin your hiring process!
You may be terrible at hiring data scientists if you’re new to it. Undoubtedly, hiring stands as a massive responsibility for a manager, a rather crucial one. A single poor recruit can significantly affect your team’s productivity, undermine morale, reduce team retention, and increase your workload as a manager.
So, here we present the best tips to help you Hire Data Scientists who will truly complement your company!
Subscribe to Testlify
Stay up-to-date on everything related to talent assessment and recruitment with our weekly content digest and email updates!
Who is a Data scientist?
As defined by UC Berkeley, Data Science is the process of identifying relevant questions and gathering data from diverse data sources. It further includes organizing the data, translating results into solutions & communicating their discoveries in a manner that benefits business decisions. In essence, data science uses automated techniques to evaluate vast amounts of data to derive insightful and reliable knowledge.
So, who is a data scientist? A data scientist is involved with developing algorithms, predictive models, and constructing data modeling processes. Data scientists can therefore devote more time to creating tools, automation systems, and data frameworks.
Now you may wonder whether a data scientist is the same as a data analyst. The answer is a big NO. A data scientist can be more focused than a data analyst on creating new tools and techniques to obtain the information a company needs to tackle challenging problems.
To comprehend the implications of the data, it is also essential to have business intuition and critical-thinking abilities. A data scientist is someone who not only understands math and statistics but also has the hacker abilities to solve problems creatively, as believed in the industry.
Why should you hire data scientists?
When you Hire Data Scientists who are right for your company, they can bring value to your company in myriad ways.
How? Here are a few ways they can help.
- Better decision-making: A skilled data scientist can use the power of data to enhance your company’s decision-making.
- By monetizing your data: Hiring a data scientist is a step toward monetizing your data, which is a significant source of income for many of today’s top businesses.
- Having a deeper understanding of the customers: A data scientist can assist your organization track any changes in client behavior, provide you with a deeper insight into your customer base, and help you optimize your business model.
- Offering unique insights: Data scientists discover unique ideas through efficient data analysis that were previously unreachable by human leadership alone.
- Expand your business: Data scientists can help in locating new potential markets for your products or services. For instance, they could examine advertising efforts and identify the kind of new clients that a specific project brought in.
Hire Remarkable Data Scientists with a Recruiting Revolution
Many businesses are scrambling to assemble a Data Science team or Hire Data Scientists for the very first time. It is easy to appreciate their enthusiasm given that they want to use data to develop to stay competitive with the market.
Yet these early blunders and false starts are costing businesses a tremendous amount of opportunity, taking up 20% or more of a data science team’s time, and data scientists are leaving these companies in only a few years.
Most managers consider themselves fortunate if they are even 50% accurate in a traditional hiring procedure. But that shouldn’t be the case. In fact, according to the hiring funnel, 90% of the hiring should include exceptional candidates while it should take no more than 10% of the team’s time.
But how can you achieve it? Just follow the right steps.
Building a talent funnel
You may need to take on the duty of contacting candidates and creating a pipeline of talents depending on the size of the data science team and assess data scientists. To find talent, managers in larger organizations can collaborate with internal recruiters or even outside recruiting firms.
The needs of the hired individuals, such as the number desired and the traits of such candidates, must be communicated by the managers. Candidate profiles may include information about prior experience, education & certifications & a candidate’s tech stack, skill set, and familiarity with use cases.
Recruiters can launch their marketing, advertising, and outreach activities on recruiting platforms using this information as a preliminary step. Often, recruiters will find people who are a strong fit but may not seek new positions in the job market.
All these candidates should be kept in a database so that recruiters can actively contact them at a later time and assess data scientists. Employee referrals are another reliable resource for finding qualified individuals.
An internal employee referral program that rewards current employees for recommending potential hires from their network is frequently a successful strategy for luring the particular talent you’re looking for.
Candidates are more likely to contact your organization looking for data science possibilities if you put focused time and effort into enhancing the profile of the data science team.
Defining roles
Good candidates are more likely to apply for positions with clear job descriptions that include a list of prospective data science use cases, a list of necessary skills and tech stack, an overview of the day-to-day work, as well as details on the interviewing process and timelines.
Writing precise & accurate job descriptions is highly important through the disregarded aspect of luring prospects. The more detail and clarity you provide upfront, the more likely the candidates will have enough knowledge to decide whether the position is right for them and whether or not to apply.
If you’re having trouble coming up with one, you can start with an existing job description template and then modify it to suit the business requirements of the team. Likewise, it’s crucial to avoid overstuffing a job description with every qualification or experience you hope an applicant will have. You will have a smaller pool of candidates as an outcome.
Instead, concentrate on the knowledge and abilities that are truly essential. The ideal applicant will be able to learn new skills while working.
Interviewing candidates in the right way
The interview process for data science roles is still relatively unstructured compared to software engineering interviews, and data science candidates are frequently unsure of what the interview process entails.
Applied scientist, research scientist, and product data scientist are some of the more recent, more specialized positions that have resulted from the evolution and transformation of the professional level of data scientist, which has only been around for a little more than ten years.
Because multiple jobs are classified as data science, a data science manager can tailor the interviewing process with the relevant assessment of Hire Data Scientists to the particular resource they are looking for!
Data scientists may understand various fields, and one or more second-round interviews may focus on fundamental competencies like programming, machine learning, deep learning, and more. Before inviting applicants for second-round interviews, one or more screening rounds can be held to save time.
These preliminary interviews, which can be conducted electronically, include a thorough assessment of Hire Data Scientists, assessing the applicant’s background, projects, career trajectory, and reasons for wanting to work for the organization.
A case study or technical assessment of Hire Data Scientists is frequently used to better understand a candidate’s approach to problem-solving, ability to handle uncertainty, and practical abilities. It gives the business valuable insight into the candidate’s potential for success in the role.
Shortlisting candidates based on performance
You must plan a debriefing session following the technical evaluation and second-round interview. Each interviewer expresses their opinions based on their interactions with the candidate during this meeting and takes a final call regarding talent recruitment.
The interview panel may give contradictory opinions about some candidates who do well in some interviews but not so well in others. In situations like this, you must exercise judgment in deciding whether to hire that specific candidate or not.
While most interviews concentrate on a candidate’s technical data science skills, it’s also crucial for interviewers to use the candidate’s time to assess data scientists based on soft skills like communication, clarity of thinking, attention to detail, problem-solving ability, business acumen, and leadership values.
Make an offer
The data science manager must create a final decision following the debriefing and communicate it to the recruiter with a salary budget. Without a recruiter, the management can offer the job role to a candidate without further delay.
When making and communicating the decision, it’s critical to act promptly, particularly if prospects attempt many interviews. Companies that hire quickly and adaptably have a competitive advantage that candidates value and are considered when making decisions.
Because there is intense competition for top data science talent, your strategy should make sure that prospects go through your funnel as rapidly as possible, maintaining momentum and lowering the likelihood that they choose a competing offer.
Moving quickly demands a streamlined procedure that enables you to increase both your confidence and quickness. To maintain your advantage, actively alter your system while investing in tools and logistics to track how long applicants remain in each level of your funnel.
Hire Data Scientists Faster with Testlify
So, what does it cost to hire data scientists? Let’s look more closely:
A data scientist in the United States typically earns $117,345 a year. In the UK, a data scientist’s income may range from more than $75,000 to more than $122,000. Average incomes tend to be lower in certain other Eastern European and Asian nations.
Given how costly it is, your hiring process must therefore be revolutionary as well. In a competitive job market for data scientists, having a strong talent pool and a quick, unbiased, and organized hiring process can provide businesses an advantage.
In light of this, it’s critical to assess applicants based on their performance and work skills. By providing a variety of skills assessments for Hire Data Scientists to locate the ideal candidate for the position, Testlify assists you in filling this gap. They are quick and to the point, giving you more information about their competencies and improving the candidate experience.
With a data-driven approach of Testlify, which advances past the conventional hiring process to one like that described above, you can hire data scientists who have the hard and soft skills you need, fit in with the culture at your organization, and come to work proved to be the greatest fit for you.
