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Published: Jun 15, 2023 13 min read
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With the growing focus on data-driven decision-making and the need to leverage insights from vast amounts of information, businesses are increasingly turning to highly trained professionals who specialize in gathering and analyzing data. This has led to data scientists becoming a highly sought-after profession for companies looking to gain an edge in the marketplace.

In this comprehensive guide — prepared in collaboration with ZipRecruiter, one of the largest and best job sites for employers and employees alike — you'll find everything you need to know about finding great candidates and how to hire data scientists that are a great fit for your organization.

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What is a data scientist?

A data scientist is a professional who specializes in the extraction, cleaning, analysis and visualization of large amounts of data. Traditionally, organizations and universities were the primary employers of data scientists. Now, however, data scientists increasingly work for companies in business-oriented roles as these organizations seek to draw insights from data and inform decision-making with large quantities of information.

What does a data scientist do?

Data scientists typically start by extracting data from multiple sources, ranging from complex databases to surveys and web traffic. Once they collect this information, they clean it up by organizing it into a usable format before analyzing it to uncover trends and data-driven insights. Data scientists then interpret their findings, often by developing visualizations or creating reports to present the results, and provide recommendations to the key decision-makers of their organization.

How to hire a data scientist

There are several components to the hiring process for data scientists. By properly preparing for each of these steps, you can make sure you find the right data scientist to help you achieve your specific goals.

Decide which type of data scientist you need

There are various types of data scientists, such as software programming analysts and business analysts. To find the most suitable one for your organization, you need to determine the type that suits your requirements.

For example, if you need someone to analyze customer behavior data and develop predictive models to inform future business decisions, you’ll likely be looking for a business analytical practitioner with a background in statistics. On the other hand, if you need to build data pipelines or create machine learning models, a software engineering analyst might be the best fit.

The following sections provide an overview of some common types of data scientists.

Business analytical practitioners

Business analytical practitioners have experience in business operations and can leverage data to help companies identify opportunities for growth, improve processes and make smarter decisions. These data scientists possess knowledge of basic statistical techniques and have the ability to develop predictive models to aid their analysis. Their analysis may involve predicting customer demand, assessing the efficiency of marketing campaigns or anticipating future industry trends. These professionals may also consider external factors like political and economic situations while making their evaluations.

Software programming analysts

Software programming analysts possess the technical skills to create data pipelines, construct machine learning models and formulate algorithms that allow organizations to automate tasks. They exhibit proficiency in programming languages such as Python, R or SQL, which enables them to build intricate systems that can efficiently process and analyze large quantities of data. Additionally, they create databases and organize data so an organization can minimize expenses and optimize its performance.

Spatial data analysts

Spatial data analysts specialize in analyzing geographical data and creating maps to visualize their analyses. They typically have a background in geographic information systems (GIS). Spatial analysts create interactive maps that provide valuable insights into the location of customers, competitors and other business entities. They also have a deep understanding of GIS technologies and can develop applications that allow organizations to manage and analyze their geographic data.

Quality analysts

Quality analysts work to ensure that data is accurate and fits the requirements of a given use case. They’re skilled in finding errors in data sets, identifying potential sources of bias or missing information and recommending solutions for improving the quality of data. They may also have experience in developing quality assurance processes and standards that allow organizations to monitor the accuracy of their data over time.

Actuarial scientists

Actuarial scientists specialize in assessing risk and probability, which makes them highly sought after within the insurance and finance industries. These data scientists use complex mathematical models to evaluate potential losses and allocate resources accordingly. Outside of the finance industry, they can also be used to assess the risk of a given project or business venture and determine the best course of action.

Determine whether you need them on a part-time, full-time or freelance basis

Whether you need a data scientist on a part-time, full-time or freelance basis depends largely upon the scope of the project, the duration that you need the data scientist for and your budget. For example, if you are only looking for help with a temporary project, it may make sense to hire a data scientist on a freelance or part-time basis. Alternatively, if you are looking for someone to help develop and manage an ongoing data analytics program, it may be more cost-effective and beneficial to hire a full-time data scientist.

In some cases, you may even be able to hire a data scientist as an intern or apprentice who can gain valuable experience while helping your organization with a specific task. Ultimately, you'll need to consider the scope, timeline and budget of your project before determining which route is best for you.

Search for prospective candidates or have them come to you

Once you've determined the type of data scientist you need, you can begin searching for prospective candidates. One effective approach is to use the best job posting sites for employers to post job openings and attract top-tier talent. Top employment marketplaces such as ZipRecruiter allow you to have listings for both full and part-time jobs. Depending on your hiring budget and timeline, you may also opt to reach out to personal contacts in the industry or attend data science conferences and meetups.

To simplify and automate hiring tasks, some organizations use recruiting software and applicant tracking systems (ATS) to automate tasks such as resume screening and application data entry. If your organization decides to do this, make sure to follow the best practices for using an ATS, such as including job-specific keywords, creating a clear job description and using data analytics to measure hiring efficiency.

Know what data scientist skills and knowledge you want your new hire to possess

Whether you're looking for an entry-level data scientist or someone with more experience, understanding what specific data scientist skills and knowledge you want your new hire to possess is crucial to finding the right employee. Even within the same job title, data scientists can possess a variety of degrees, certifications and specialized skill sets in a range of areas, such as machine learning to advanced analytics.

That's why you should identify the exact knowledge and skill sets you need for your project. By defining your needs, you can recruit the right person for the job while developing a clear set of expectations and performance criteria for your new hire.

Write a detailed and compelling data scientist job description

Now that you have the right qualifications in mind, it’s time to write a detailed and compelling job description. Your job description should highlight the main responsibilities of the role as well as any specialized skills required for success. Be sure to include a comprehensive list of qualifications and experience, as well as the core competencies and professional traits that you’re looking for in a data scientist. It's also a good idea to include the estimated starting salary and any benefits that the employee will be eligible for. You might refer to ZipRecruiter's data scientist job description template as a starting point.

You can also post this description to a career page on your website that gives potential candidates a better understanding of the company and its culture. When considering how to create a better career page, remember that this is a great opportunity to show potential hires why your organization is the right fit for them and why they should consider joining your team.

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Ask the right questions during the interview

Asking the right questions in an interview is the best way to evaluate a data scientist's experience and skill set while developing a feeling for how they might fit into your organization. Inquire about their past experience, passion for data science and any relevant technical skills. You should also ask questions about their workflow and methodology, as well as their understanding of the data and its potential applications.

The following are some examples of questions you might ask in an interview:

  • What do you consider your biggest accomplishment as a data scientist? The candidate's response to this question can help you understand the type of projects and challenges they are most comfortable with along with their overall level of experience.
  • How would you go about cleaning up and organizing a large, messy dataset when under a very short time constraint? This behavioral interview question will provide insight into their data-wrangling skills, workflow, methodologies and preferred tools. It can also show you how they handle pressure and tight deadlines.
  • What tools do you use to present data and how do you determine what visualization works best? Asking this question can help you understand the candidate's comfort level with data presentation and the type of visualizations they prefer to use.
  • What interests you most about data science? This broad inquiry can provide valuable insight into their motivation and passion for data science.

Onboard your top choice

After selecting a suitable candidate and confirming their application details through the best background check sites, plan their induction and support them in their new position. Create a detailed onboarding program that outlines their job responsibilities, introduces them to team members and familiarizes them with your company's principles and etiquette.

Additionally, scheduling periodic feedback sessions with the new employee is beneficial for assessing their progress. You may also assign them a mentor or coach who can provide guidance and help them become better acclimated to the company, their team and their specific role. Such practices are great for professional growth and improving employee retention.

Data scientist FAQ

How much does it cost to hire a data scientist?

As with many other highly technical roles, the expenses associated with hiring a data scientist can vary greatly depending on the methods you use to source candidates, your location and the necessary experience and qualifications, among other factors. Hiring a data scientist can involve several rounds of interviews and other assessments, so the costs associated with each hire may be higher than for more traditional positions. Generally speaking, hiring costs for a data scientist range from a few thousand to tens of thousands of dollars.

How much do data scientists make?

Recent data from the Bureau of Labor Statistics indicates that the median annual salary for data scientists in the United States is $103,500, or $49.76 per hour. The 75th percentile had an annual salary of $136,600, or $65.68 per hour, and the 25th percentile received an annual salary of $77,140, or $37.09 per hour.

How can data scientists help businesses achieve their goals?

Data scientists can apply advanced techniques like analytics, machine learning and artificial intelligence on large datasets. This helps decision-makers obtain useful insights and predictive models that can identify customer needs and growth opportunities. Companies that fail to use data scientists may be at a disadvantage compared to those that do, especially in our age of big data.

Data analyst vs data scientist: What's the difference?

Data analysts are primarily responsible for gathering, cleaning and analyzing data. They develop reports and insights on customer behavior or trends in the marketplace. Data scientists, on the other hand, are more focused on applying advanced analytics techniques and machine learning algorithms to uncover hidden patterns and trends in data. Data scientists are also typically responsible for developing predictive models that deal with the unknown, such as predicting customer churn or forecasting market trends.

Data engineer vs data scientist: What's the difference?

Data engineers are responsible for designing, developing and maintaining data systems and pipelines. They focus on the infrastructure side of data science and build robust systems that can ingest, process and store large amounts reliably. Data scientists, in contrast, use those systems to analyze and uncover new insights from data.

Summary: How to hire data scientists

Hiring any technical role is an involved process. This is especially true for filling data scientist jobs, which require a level of expertise and experience that can be difficult to find. The more preparation and research you do in advance, the less you will have to worry later on about how to retain employees or whether you have the right people in place.

Effective preparation for the hiring process includes determining the type of data scientist you need and what type of skills they should have, creating an accurate and attractive job description, finding candidates from the best sources and asking the right questions during interviews. By following best practices, you can ensure that you are hiring the best possible data scientist for your team.