A step by step guide on how to hire data scientists

complete guide to hiring data scientists
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The number of businesses that hired data scientists increased from 30% in 2020 to almost 60% in 2021. Organizations are increasingly looking for top data scientists to lighten their data analytics load.

Is your organization among those searching for exceptional data scientists? Are you looking for ways to recruit data scientists with ease? Here’s the blog post you need.

Discover all you need to know about hiring data scientists in this ultimate guide, and learn the key strategies you can use to make your hiring process easy.

What do data scientists do?

As explained by Master’s in Data Science, data scientists are big data wranglers who gather vast amounts of data, which can be structured or unstructured.

Data scientists will then analyze and make inferences from the data. They may need to refine unstructured data, including data from social media channels or email exchanges.

Not only do data scientists look for trends or patterns in data, but they also use their comprehensive industry-related knowledge to come up with clever business solutions and answers to complex problems.

What’s the difference between a data scientist and a data engineer?

Although data scientists and data engineers are similar, there are some distinct differences. For example, data scientists look for patterns in big data to gain insight into trends, make predictions, and inform decision-making.

On the other hand, data engineers focus on building the infrastructure businesses use to understand data. They also ensure efficient data collection, transform raw data into usable formats, and optimize databases for streamlined analysis.

What can good data scientists achieve for your business?

Some of the complex tasks that good data scientists can complete for your business include performing in-depth analyses of your transactions and procedures, assisting in predicting future business success, optimizing your business, and informing you of simple ways to keep your customer turnover low. Here’s how.

what can good data scientists achieve for your business

1. Expertly analyze your business transactions and procedures

Data analysts are a type of data scientist who will analyze your transaction data. They can also deduce how and why your current business efforts are giving you specific results and how these results affect your organization. A top data analyst uses tools like SQL to find patterns in large amounts of data effortlessly.

2. Provide support when making predictions of future business success

Crystal balls aren’t going to help you make predictions of future business success, but data analysts can undoubtedly give you insights in this respect. One category of data analytics that data scientists tap into to achieve this is predictive analytics, with which they can make predictions about your business based on previous data.

A data analyst will be comfortable using tools and analytics models to gain future insights into the performance of your business with ease.

3. Make accurate forecasts to effortlessly optimize your business

Business optimization doesn’t have to be a challenge—a data scientist can optimize many business areas, including product innovation, customer service, and marketing efforts. As Enterprise IT World explains, data scientists achieve this by analyzing different types of data, including:

  • Sales statistics and data

  • Customer service data

  • Market data (taking note of anomalous data)

4. Keep customer turnover low

Good data scientists can help you keep customer turnover low by analyzing data and determining how the information relates to your audience’s behavior. 

They can determine your customers’ interaction preferences and consumption habits. This, in turn, can lead to better business decision-making that matches the requirements of your customers. 

Writing a data scientist job description: 4 essential tips

So, if you’re convinced that data scientists can make a significant difference to the progress of your business, you’re probably now eager to write the job description!

Here are the four tips you should keep in mind when writing a job description for a data scientist role.

For more detailed information on writing job descriptions for data-related roles, check out our guide on writing a data engineer job description.

tips to writing a job description

1. Summarize the expectations and why the role exists

A data scientist job description begins with a powerful summary of the position, including a little bit about why the role is essential. The summary is your opportunity to give potential candidates a bit of context and insight into the role. Explain how the position relates to your organization and what makes the position unique.

2. Avoid using jargon 

According to Workable, because many candidates skim-read job descriptions, jargon and confusing phrases will turn candidates off and alienate them.

Instead of saying something like: “We are on the hunt for a math genius who is adroit at complex linear algorithms and logistic regression algorithms, is knowledgeable of and competent at using intricate decision trees, and understands the significant benefits of using linear discriminant analysis,” aim for more concise wording, such as the following:

“Our organization is recruiting a data scientist to clean and analyze our data.”

3. Keep the job description short

Candidates skim job descriptions, so it’s best to avoid making them wordy when hiring for a data science job.

Job descriptions for finance and data jobs contain an average of around 450 words, according to The Muse, and Undercover Recruiter suggests that your job description should be around 500 to 600 words.

That’s why it’s a good practice to keep a data scientist job description short. Aim to write a compelling job description that has 400 to 600 words. Check out our guide to writing great job descriptions.

4. Mention the employee experience, and describe the benefits of working with you

Describing perks and benefits is one of the best ways to engage your candidates with a job description. When you’re thinking of ways to sell perks and benefits to candidates, note the difference between “perks” and “benefits”.

“Perks” are usually cash or salary related, whereas “benefits” are a form of non-cash compensation that covers employees’ basic needs.

In addition to health insurance, some of the top benefits you can mention in a job description include:

  • Insurance for dental care

  • Life insurance

  • Paid days off for personal reasons or vacation

  • Plans for retirement savings

Data scientist recruiting: 3 sites to find data science engineers

Now that you’ve written the job description, you are probably wondering where to post it and how to source data science engineers. Three of the most popular sites for sourcing data scientists are Stack Overflow, Dice, and AngelList.

1. Stack Overflow

Stack Overflow has an excellent niche job board section that is aimed at developer and data scientist positions. It also has a feature for specifying which skills you’re looking for.

For instance, if you are hiring a senior data scientist, you can add specific skills tags to the preview of the job description, such as “Python,” “neural language programming,” or “machine learning”. This option makes it simpler for candidates to self-evaluate and compare their skills with the skills you’re looking for.

2. Dice

Dice is a niche and unique tech job board that helps you source candidates from a comprehensive database featuring 97% confirmed tech professionals. This job board also attracts one million visitors a month.

Individual job posts on the Dice platform cost $495 each. Two job posts cost $450 each, and three job posts cost $425 each.

3. AngelList

Although not strictly a niche job board, AngelList lets you source candidates, post job listings, and manage and track your recruitment efforts with a basic applicant tracking system for startups. 

The platform has a network of more than eight million users, who are ideal candidates for tech startups.

Posting a job is completely free on the AngelList platform, but the Pro version offers you unlimited access to all features and candidates, as well as several additional features, for $250 a month. 

Skills and qualifications to look for in a data scientist

Having posted the job description, you may now be getting applications for your vacancy and will need to prepare for candidate evaluation. When evaluating candidates, don’t forget to keep an eye out for the following skills.

skills and qualifications to look for in a data scientist

1. Business knowledge

In addition to technical skills, all exceptional data scientists have good business knowledge and insight. Not only must they understand the key objectives and goals of the company, but they should also be capable of coming up with ways to achieve those objectives effortlessly and cost-effectively. 

Have your candidates researched your organization and what it is trying to achieve? Can they come up with initial suggestions to help it achieve those goals? 

2. Proficiency with databases and programming knowledge

Since using databases and working with computer programs are a critical part of a data scientist’s role, you’ll need to evaluate whether your candidates have these skills. 

For instance, your candidates should have no problems when finding solutions to fix queries in a database. They should also be competent with programming languages, such as Python, JavaScript, and SQL.

3. Analytical skills

Because it is crucial for data scientists to have a deep curiosity about solving problems and finding solutions, your candidates should be able to look beyond the data to determine what it indicates about your organization’s performance.

4. Knowledge of and experience with Excel and PowerPoint

Although it’s not suitable for dealing with big data sets or complicated algorithms, Excel is still a critical tool for people in data science positions, because it helps to facilitate the analysis of smaller data sets—so, data scientists should be proficient in Microsoft Excel. 

And since an essential data science skill is sharing recommendations for a business based on analytical insights, PowerPoint is another critical tool that your candidates must be comfortable using.

Are the candidates in your talent pool able to use Excel and PowerPoint efficiently?

qualifications and skills to look for in a data scientist

5. Mathematical skills, algebraic knowledge, and knowledge of statistics

Mathematical, algebraic, and statistical knowledge are also essential for data scientist candidates, especially knowledge of statistics, probability, and linear algebra in the context of machine learning. The specific topics your candidates need to be familiar with include:

  • Matrix determinants

  • Negative predictive value

  • Standard deviation/variance 

  • Derivatives and gradients

  • Probability distributions

  • Gradient descendent algorithms

Pay close attention to candidates who show a comprehensive understanding of these statistical, algebraic, optimization, and mathematical topics. 

6. Big data and machine learning knowledge

According to Edureka, data science involves data mining, big data, and machine learning. Mining vast amounts of data efficiently can be a challenge, so data scientists need machine learning and algorithm knowledge.

Are your candidates able to describe an example of how their knowledge of big data and machine learning has helped them mine data successfully and gain insights into a company’s performance?

7. Critical thinking skills

As explained by Tableau, critical thinking in the data science field refers to seeing all angles of a problem and considering the data source. Data scientists should always be willing to question the data, which is why curiosity is a critical thinking sub-skill they should have.

Can your candidates show that they can ask the right questions when handling and assessing data to reach conclusions about your organization?

8. Problem-solving skills

Proactive problem solving is precisely what data science is all about. But although unraveling the most complex issues in data science is often the most valuable outcome for organizations, it can be a big challenge.

Do your candidates recognize which problems are the right ones to tackle? Can they add value by solving the most challenging issues?

9. Communication skills

These soft skills are essential for data science roles. Your candidates should be comfortable breaking down, presenting, and explaining the complex data they have gathered and cleaned and what it means for your organization.

And because data scientists have to explain their data insights to team members who might not have technical data science skills, your candidates will need to use the following communication best practices to communicate clearly with cross-functional teams without confusing them:

  • Use language that is appropriate for the audience

  • Avoid using a dense visualization for essential data insights when a basic table will suffice

  • Give structure to the topic when presenting it to an audience

  • Use peer reviews to make sure the details and inferences are coherent

Communication also involves listening to team members. Can your candidates show that they’re skilled active listeners in peer reviews or when answering questions from their audiences?

Skills testing: Which hard skills tests should you use to assess data scientists?

You’ll benefit from comprehensively evaluating your data scientists’ skills, and skills testing is one of the best methods for doing this. There are plenty of skills tests that you can use to assess your candidates’ hard skills, such as Python, Java, coding algorithms, and data science skills tests.

Python skills tests

Use Python skills tests to assess whether your candidates have the crucial Python skills required for web scraping, creating predictive models, or saving time when creating reports.

Java skills tests

Find out if your candidates can use Java efficiently to analyze and process data, carry out statistical analyses, and visualize data with a Java skills test

Algorithmic skills tests

In addition to linear regression, some of the top algorithmic skills required by data scientists include decision trees, logistic regression, and the k-means clustering unsupervised machine learning algorithm. Select from a range of algorithmic skills tests to assess your candidates’ abilities. 

Data science skills tests

It’s also crucial for data scientists to have general data science skills. Use Data Science skills tests to assess whether your candidates can handle statistics, understand the complexities of machine and deep learning, and recognize how neural learning is essential in the context of your organization’s data.

Challenges of recruiting data scientists and how to overcome them

A few challenges you may face when hiring a data scientist include engaging candidates to avoid losing them to the competition, searching for a specific, hard-to-find skill set, finding candidates with the right soft skills, evaluating candidates if you lack experience in the field, deciding between academic knowledge and work experience, and upskilling if recruiting internally.

You could lose a data scientist candidate to the competition

Your best candidates will have desirable skills that your competition might also be looking for, so these candidates will typically be highly sought-after.

There’s every possibility that top data scientists are interviewing with other organizations, so you must engage your candidates to avoid losing them to the competition.

Here are three ways to engage your data scientist candidates:

  1. Develop an engaging career page for your organization. Feature employee testimonials on your career page that explain the benefits of working for your organization, such as the potential for career progression.

  2. Be prepared to mention the culture of your organization. When you describe your organization’s culture, don’t just use clichés, such as “we work hard and we play hard”. Show specific examples of the successes your organization has achieved through its hard work and the ways your team unwinds afterward.

  3. Make the application process open, transparent, and straightforward. There’s nothing more frustrating than a lengthy application process. Since the ideal timeframe for hiring a candidate is between two and four weeks, any timeframe beyond four weeks could mean losing those A-grade candidates to another company.

A specific set of technical skills is required but difficult to find

Because data scientist roles require a specific, high-level technical skill set that might be hard to find, candidate sourcing can be challenging. Just when you think you’ve found the right candidate, you may discover they lack the right programming skills for your organization.

According to Undercover Recruiter, these are some of the best ways to hire specialized data science talent:

  1. Use industry-specific language relating to the data science field in the responsibilities section of the job description. Although you shouldn’t use too much jargon in the introduction, using technical language to describe the key responsibilities can help candidates self-evaluate their skills for the position.

  2. Evaluate candidates using a thorough evaluation process, which includes carrying out a face-to-face interview. You can perform part of the candidate evaluation when you invert the interview process, as you can write down all the questions your candidates ask to see how committed they are to your open role. 

Use this method if you invite your candidates to visit the company headquarters for an in-person interview. 

  1. Target your audience using niche job sites. Niche job boards are typically industry focused. Not only do they help candidates who are looking for open roles in targeted industries, but they will also make candidate sourcing easier for you, since niche job boards put job announcements in front of new, focused audiences.

Candidates with technical skills may lack soft skills

Soft skills are essential for data scientists, so make sure that you evaluate them before deciding on a candidate. One way to assess soft skills is to use skills-testing platforms.

For example, if you want to know if your candidates have top time-management skills, use a Time Management skills test to evaluate how well they can prioritize different tasks, plan toward task completion, complete the tasks, and then take feedback on board.

If you want to know if your data scientists are good problem solvers, use a Problem-Solving skills test to determine if they can prioritize tasks, interpret data, use their logic to make decisions, and analyze information to gain insights into your organization’s performance.

You may be a non-technical recruiter and unaware of how to evaluate skills

Data scientist positions require technical expertise, so if you’re a non-technical recruiter hiring for a data scientist position, you might not know how to evaluate your candidates’ skills thoroughly.

If you’re not sure what specific algorithms mean when assessing data or reaching conclusions, one of the best solutions is to use skills testing. Since experts in the field create skills tests, they will help you to discover if your candidates have the skills required for the position.

Choosing between academic knowledge and work experience

It’s also challenging to choose between candidates with strong academic data-science qualifications and candidates with years of work experience in the field. 

Although Springboard suggests that candidates can learn data science without an advanced degree and that previous experience isn’t needed to become a data scientist, academic knowledge and work experience can help candidates learn the algorithms and programming skills your vacancy requires. 

The good news is that you won’t need to choose one over the other with a skills-testing platform. Whether your candidates have gained their knowledge and skills through academia or work experience, you can focus on evaluating the exact skills that the position needs and not necessarily how your candidates have learned them. 

If you recruit internally, upskilling current employees can be challenging

You will need a benchmark from which to plan training sessions if you’re hiring internally, so one other challenge when recruiting data scientists is upskilling current employees.

But you can even use skills testing to assess your company’s current data scientists to see what level they’re currently at. Then you can put together training sessions to help them develop their skills. 

Interviewing a data scientist: 3 tips for learning more about candidates

After you’ve evaluated your data scientists’ skills using skills-testing platforms, you can focus on interviewing candidates using situational interview questions, behavioral interview questions, and a structured interview process.

Ask candidates to describe how they overcame a recent problem

Since problem-solving skills are critical for data scientist roles, it’s crucial to check if your candidates have these skills. You’ll learn more about how your candidates could fit into your organization by asking them a situational interview question, such as how they overcame a recent problem.

Recommended reading: 8 problem solving interview questions for identifying top talent

Candidates who can describe in detail the situation they were faced with, the task they needed to solve, the actions they took, and the results that came from their efforts are candidates you should consider bringing forward into the next interview stage.

For instance, you might ask your candidates about a situation in which they had to solve a challenging issue and look for answers that describe the results of their actions.

Ask behavioral interview questions

Because critical thinking is essential for data science positions, it’s important to assess this skill. Behavioral interview questions can help you learn more about how a candidate’s thought processes are linked to their behavior.

For example, you could ask your candidates questions about how they prioritize tasks and the steps they take to handle multiple tasks simultaneously.

Stick to a structured interview process when interviewing a data scientist

Since structured interviews have several advantages over unstructured interviews, they are the best option for interviewing candidates. 

Structured interviews are a type of interview that relies on a set of standardized questions. These questions are usually composed before the interview begins and are different from unstructured interviews, in which the interviewer does not prepare a set of pre-planned interview questions.

A structured interview process can help you achieve the following:

To conduct a structured interview, ask closed questions to limit the potential responses of your data scientist candidates and make it simpler to analyze and compare their answers.

9 specific interview questions to ask data scientist candidates and answers to listen for

Since you might also be wondering which specific interview questions to ask your candidates, here are nine potential sample questions for you to choose from:

1. How does supervised learning differ from unsupervised learning?

Candidates who recognize that feedback mechanisms feature in supervised learning but not unsupervised learning are candidates to watch. 

Can they also describe some of the algorithms used for supervised learning (such as decision trees or support vector machine algorithms) compared with unsupervised learning algorithms (such as hierarchical and k-means clustering)?

2. What is your experience with open source, and have you completed any open-source projects?

If your candidates can collaborate with others on projects, such as open-source projects, they’ll have no problem demonstrating their collaborative mindset when they answer this interview question. Do your candidates have specific examples of such projects, and what knowledge have they gained from their collaborative efforts?

Open-source projects also help data scientists to keep their fingers on the pulse of new changes in programming, which can benefit them in a data science role. So look for candidates who can cite examples of what new ideas they have learned.

3. Can you describe the differences between deep learning and machine learning?

Your candidates’ responses to this technical question will help you to discover if they understand how data and machine learning and data and deep learning are linked. Do they know that algorithms specific to machine learning need structured data and that deep learning requires artificial neural networks?

Take note of candidates who can also give real-life examples of how deep learning functions, such as cars that self-drive, chatbots that can respond to basic interview questions, and image processing in computers.

4. Which tools do you consider the most important for data scientists?

Since using tools is an essential part of a data scientist’s role, ask your candidates this question to learn if they know which tools to use for specific tasks.

Some of the tools they might mention in response to this question include those that facilitate data visualization and programming languages. Give bonus points to candidates who can outline the benefits of specific tools, including programming languages and analytics tools.

Although not every candidate will be proficient in every programming language, they should know how to develop an understanding of the programming languages your organization uses. 

5. What experience have you had with a team that has multiple skill sets?

Your successful candidate will work alongside cross-functional teams and frequently collaborate with other team members, including technical and non-technical teams. For instance, a data scientist should work with marketing, product, and developer teams.

For this reason, you can ask this question to gain insight into their experience with teams with multiple skill sets.

Take note of candidates who have worked with a range of clients, enjoy collaborating with team members who work in different departments and have varying levels of seniority, and have worked with teams with different skill sets.

6. What is the best statistical software, in your opinion?

Top data scientists should know how to use a large selection of go-to statistical software to help them accomplish tasks. Bonus points if your organization uses the software they mention.

However, since not all candidates will use the same software you do, also keep an eye out for those candidates who can adapt to unfamiliar statistical software.

7. What are the disadvantages of the best statistical software?

Not only should a data scientist candidate be able to identify several types of high-quality statistical software, but they should also be able to evaluate the examples they provide in terms of their negative functions and provide examples of how these functions would affect their role.

Can they tell you the limitations of the best software and then explain how they would work around those limitations? 

8. What is the worst statistical software, in your opinion?

Your candidates might mention several examples of statistical software they would prefer to avoid. But if there is a circumstance in which they must use this software, how would they adapt to it?

Top candidates will either give an example of how they have adapted to unfamiliar statistical software in the past or outline the steps they would take to get accustomed to using it.

9. What are the advantages of the worst statistical software?

Despite the flaws in their least favorite statistical software, the best candidates will know about its advantages. They will find it easy to evaluate not only its flaws but its merits as well.

Onboarding: What steps should you take to onboard data scientists?

So, you’ve hired the right candidate for your team. It’s time to onboard your new data scientist.

Three crucial steps you can include in your onboarding process are assigning a mentor to them, giving them a smaller project to start them off, and helping them learn who the stakeholders and team members are.

  1. Help your new hire get to know key stakeholders and teams in the organization

A critical part of your onboarding process is introducing your new hire to the rest of the team and the key stakeholders. Your team’s size will determine how you do this, but it might involve taking a lap around the office or sitting down for lunch so that your new hire can put faces to names. 

  1. Pair them with a mentor or buddy

As Christer Bodell states on LinkedIn, even seasoned employees can struggle without a certain amount of onboarding, and part of the onboarding process should include mentoring.

Mentoring gives new data scientists the confidence to ask questions and helps less experienced data scientists feel more secure while enabling them to learn how their role fits into the broader organization. 

  1. Start them off with a minor assignment or project

As soon as you’ve given your new hire access to the various software applications and accounts they will be using, you should start them off with a minor assignment or project.

You don’t have to assign your new data scientist a huge project or high-priority task, but the project should give them a taste of your organization.

Data scientist hiring checklist: Key things to remember

All of this may seem like a lot to remember, so here’s a data scientist hiring checklist that contains the key things you’ll need to include in your hiring process:

  1. Write a short, jargon-free job description that’s 500 to 600 words in length and describes the perks, benefits, values, and culture of your organization.

  2. Source data scientists by using niche job boards, such as Dice and Stack Overflow.

  3. Search for candidates who have hard skills in addition to business acumen, cognitive abilities, knowledge of databases, Microsoft Office skills (Excel and PowerPoint, in particular), mathematical skills, and big data knowledge.

  4. Don’t forget that soft skills are also crucial. Search for candidates who are good problem solvers and communicators and have top critical thinking skills.

  5. Use skills testing platforms like TestGorilla to create skills assessments and evaluate your candidates thoroughly.

  6. Use situational, behavioral, and structured interview questions to learn more about your candidates.

  7. Onboard your data scientists by pairing them with a mentor, introducing them to stakeholders, and giving them a minor, initial assignment once you’ve set up their accounts and software.

Hire the right data scientists and start achieving your business goals

Since the right data scientist helps you spot trends in your data and make insightful predictions about your company’s performance, there’s no question that you’ll begin to achieve your business goals with their support.

Don’t forget to have a look at reliable skills-testing platforms to make hiring effortless, then watch as your organization begins to achieve its targets after you’ve hired the right data scientist for your team. Try TestGorilla for free.

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