How to Hire Data Scientists (Updated in 2024)

Data scientists are the key to leveraging data’s analytic and predictive power. According to Statista, the average number of data scientists employed in the companies surveyed rose to 50 in 2021 from 28 the previous year.

The hiring of data scientists is expected to grow by 35% between 2022 and 2032, and many organizations can’t risk not having well-staffed data science departments. However, this is a relatively new role, and many don’t know how to recruit for it yet.

This article explains the need for hiring data scientists today and where to find good candidates. We also look at how to recruit them, write job descriptions that attract the right applicants, interview prospective data scientists, and assess candidates.

Organizations have different objectives when hiring data scientists. Here are a few reasons why hiring one makes sense for modern businesses.

Data-Driven Business Optimization

Businesses regularly make decisions with the hope of optimizing various functions, such as hiring, sales tactics, consumer outreach, and warehouse layouts.

Whether the decision is to implement a change or maintain the status quo, a data scientist can help determine the optimal outcome by analyzing data.

Business Forecasting

Business decisions, such as whether or not to enter a new market, can make or break an organization. The power of analytics makes it possible to make faster and more accurate predictions.

Data scientists can look at different data sets, perform analytics, and predict, based on current trends, how sales might grow or how a market is likely to evolve.

Understanding Customer Behavior

When a business understands its customers’ behavior, it can modify its products or services to better meet their needs. Furthermore, it can even influence or alter their behavior altogether.

Data scientists’ skills will be crucial in analyzing the massive amount of data required to understand the behavior of potential customers for your business. Check out this post on Interview Query on why and how businesses perform customer analytics.

Hiring good data scientists requires knowledge and expertise. Knowing where to look and how to appeal to good applicants goes a long way.

Where to Find Data Scientists

Posting on traditional job sites and hoping the right candidates will see it is an ineffective strategy for finding good data scientists. Instead, consider trying the following locations.

AI, Machine Learning, and Data Science Events/Conventions

The fields of AI and machine learning are closely intertwined with data science. Of course, data scientists are present at data science-themed conventions. However, AI and machine learning conventions are more prevalent and likely to attract top data scientists.

Many attendees will seek to expand their professional networks, while others hope to meet potential employers at these events.

Campus Events

Some of the best data scientists are still in school. Campus events like career fairs are perfect for meeting and interacting with these candidates. Many colleges now make this easier by hosting themed artificial intelligence and data science events.

LinkedIn

It might be considered old school, but LinkedIn is still an excellent place to find great candidates for data science positions. LinkedIn’s advantage is its global reach, which is especially helpful if the position is remote. You’ll get access to an international pool of talented data scientists and perhaps just the right candidate who can relocate to fill your in-house position.

Niche Websites and Job Boards

Websites tailored to data science and similar disciplines are valuable for recruiting. These sites already attract the ideal targets for such jobs; some even have job boards to make your work easier.

Interview Query is one such website. This site was created to help candidates prepare for interviews in tech-related fields, including data science. We also help companies hire data scientists and other data professionals.

Employee Referrals

Employee referrals remain a critical means of recruiting new talent. Existing employees, especially those on the data science teams, may already know highly skilled candidates who will fit in with the organization’s culture.

How to Write a Data Science Job Description

A good job description will attract great candidates while saving others the frustration of applying for a job that’s a poor fit for them. Here is how you can craft a job description that achieves this.

Explain Why the Position Exists

This describes what the data scientist does and what they are expected to achieve in this role at your organization. This description should give candidates a quick look at what the job will be like and its importance to the broader organization.

You can also include a link to a blog post, publication, or YouTube video that highlights the data science team’s work. This gives candidates an idea of the kind of problems they’ll be tackling and who they’ll be working with.

Discuss the Employee Experience

Many candidates will be interested in learning about a typical day working at your organization and the perks and benefits of the position, especially considering the high demand for data scientists.

This is an opportunity to talk up the position to the right candidate and plays a key role in attracting the best talent. If this position is new in your organization, find out how much other companies pay their data scientists on Interview Query.

Outline Key Skills

The data science spectrum is broad, and good candidates want to know their suitability for a position before spending time applying or interviewing. Your job post should include the main skills and tech stack candidates should possess.

You don’t need to include every desired skill or experience. However, the job description should offer enough clarity for candidates to self-evaluate and decide if they are a good fit. Check out this post on Interview Query to find out about the skills data scientists at different levels should have.

Keep It Short, Use Industry-Specific Language, but Avoid Jargon

According to LinkedIn, the average job-seeker only spends 14 seconds deciding whether or not to apply for a job. Keep the description as brief as possible while including enough information.

Industry-specific language is great for attracting the right people, but beware of jargon that may give false impressions about the nature of the company or job and alienate good candidates.

How to Conduct a Data Scientist Job Interview

Conducting a good interview can help you recruit the best candidates while giving unsuccessful candidates a positive experience. This matters because their opinions can determine your ability to attract great candidates in the future.

Identify the Skills to Test

The interview process should be customized to the profile of the data scientist you need. You can figure out what to test for based on the expected outcomes for that role and the skills needed to perform job functions. Essential skills for data scientists include:

• Statistical analysis
• Working with large data sets
• Coding
• Machine Learning/AI
• Mathematics
• Data visualization
• Communication skills
• Critical thinking and problem-solving
• Decision making
• Leadership

You can check out the company guides on Interview Query to see what other companies test when recruiting for data roles and even specific questions asked during interviews.

Create a Structured Interview Process

Once you’ve identified the skills and areas you’ll be testing, plan a series of interviews to ensure the most important aptitudes are covered. A structured interview process will make it easier to objectively assess and compare candidates.

Initial Screening

There should be at least one screening round to assess certain skills early and reduce the number of applicants to the most qualified ones. Screening rounds can be conducted online or over the phone.

The data science team can do the screening internally, but many companies opt to outsource this part of the process to companies such as Interview Query and HackerRank.

Second-Round Interviews

Second-round interviews typically consist of two or more interviews to comprehensively test technical skills and cultural fit. It is important to test soft skills and check if the candidate will fit in with your organization’s culture.

Technical assessments during these interviews will help evaluate the candidates’ problem-solving, critical, and creative thinking skills. They also expose candidates to the kind of problems they’ll be expected to work on.

Interview Query has a curated question bank where you can check out questions and answers suited to different data roles.

Assessment of Data Science Candidates

After the second-round interviews, interviewers can share their experiences with different candidates and give their recommendations. The hiring manager should also share their opinion.

A structured interview will make the assessment easier, especially when there are more suitable candidates than open positions.

Hiring managers may have to go with their intuition in some cases. Certain candidates perform poorly in some interviews but show great potential. Sometimes, it’s better to go with the candidate who’s a good cultural fit and has the potential to learn quickly.

Additional Tips on Interviewing and Assessing Candidates

There are other steps you can take to increase your chances of hiring a good data scientist. These include:

• Avoid an unnecessarily lengthy interview process: According to LinkedIn, the hiring process should last 3 to 6 weeks. Any longer than this increases the risk of losing good candidates to other companies.
• Work with technical experts: Data scientists must be assessed on their technical skills. If the interviewer lacks technical expertise, they should work with an expert to adequately test the candidates’ skills.
• Put a time limit on job offers: Tendering offers quickly can prevent the loss of good candidates. However, a time limit should be given for accepting the offer so the company can stick to its recruitment timeline and avoid situations where its offer is used to leverage a better offer elsewhere.

The surge in demand for data scientists has made it tricky to hire one when every business needs their expertise.

However, you’re on the right track when you know where to look, who to talk to, and how to conduct a recruitment process that objectively evaluates candidates.

At Interview Query, we can make finding your company’s next data scientist a smooth process. We have access to a global pool of talent in different data science roles and have helped place many in top companies, including Amazon, Meta, and Google.

You can post on our job board to reach thousands of candidates or get us actively involved so we can help screen candidates or train your team on questions to ask data scientists during interviews.

Also, consider OutSearch.ai if you want to hire the best data scientists without the hassle. Their AI-driven platform helps you streamline recruitment, ensuring you connect with the most qualified candidates efficiently.

There are plenty of great data scientists waiting for the right employer. Hopefully, they’ll be part of your team soon!