The world’s data output is accelerating at a staggering pace. In 2024 alone, global data creation reached an estimated 149 zettabytes, and is projected to more than double to 394 zettabytes by 2028, according to Statista. What’s behind this exponential growth? Digital transformation, IoT devices, e-commerce activity, social media use and other data-rich technologies that touch nearly every aspect of business and life.
And the momentum is far from slowing. In a recent Robert Half survey, 97% of technology leaders said their departments are participating in major digital transformation initiatives over the next two years, from cloud migrations to AI-driven automation.
This shift has fueled demand for skilled data scientists who can interpret and act on complex data in real time. For many companies, knowing how to hire a data scientist quickly has become a competitive necessity. But with demand outpacing supply, many organizations are facing critical talent shortages. Robert Half’s Building Future-Forward Tech Teams report found that 76% of technology leaders have skills gaps in their departments, with data science ranking among the top skills they lack.
How to Hire a Data Scientist: 6 Steps to Hire a Great Candidate
What is the role of a data scientist in business?
The rapid adoption of generative AI has intensified demand for data scientists—but their value extends well beyond AI. These professionals are vital to digital transformation, providing the analytical expertise needed to support initiatives across marketing, sales, finance, operations and product development.
Data scientists apply advanced analysis techniques to extract insights from both structured data (e.g., spreadsheets) and unstructured data (e.g., social media analytics). These insights can help companies:
Optimize processes and improve efficiency
Reduce costs and manage risk
Drive new product and service development
Uncover new market opportunities
Personalize customer experiences
Given their wide impact, it’s crucial to hire data scientists who combine technical expertise with an understanding of your industry’s unique challenges.
Skill sets to look for when hiring a data scientist
Given the complex and data-intensive nature of their work, data scientists need a mix of technical expertise, analytical thinking and business acumen. These professionals must be able to extract meaning from vast datasets and translate their findings into actionable insights that support strategic business decisions.
Must-have skills
Analytical thinking and quantitative analysis
Data mining and multivariate statistical modeling to uncover patterns and relationships in complex datasets for predictive modeling and other applications
Proficiency in programming languages such as Python or Java
Experience writing SQL queries to extract and manipulate data
Ability to apply machine learning algorithms to large datasets
Preferred skills
Experience with big data frameworks such as Hadoop or Spark
Familiarity with cloud-based analytics platforms (e.g., AWS, Azure, Google Cloud)
Proficiency in data visualization tools such as Tableau or Power BI
Knowledge of deep learning frameworks (e.g., TensorFlow, PyTorch)
Background in business analysis or industry-specific domain expertise
Experience collaborating in cross-functional teams to translate data insights into operational strategies
These last two points are especially important. When hiring data scientists, look for candidates who know how to combine technical skills with a business mindset and consider the real-world implications of data.
More signs the demand for data scientists may intensify
According to Building Future-Forward Tech Teams, AI, machine learning (ML) and automation initiatives rank second among the top five priorities for technology managers this year. Separate research from Robert Half found that the demand for emerging tech and IT skills, like the ability to work with AI, ML and natural language processing, has actually been increasing significantly for several years now. Those skills and initiatives are fueling hiring, with 97% of tech leaders planning to recruit professionals to support AI and machine learning projects this year.
6 steps to hire data scientists who deliver results
Recruiting data scientists, who are essential for advancing AI, machine learning and data analytics initiatives, will remain highly competitive. To succeed, you need a clear and strategic approach. The six steps below outline how to target, attract and secure top talent in this in-demand field.
1. Define your needs from the outset
Start by clearly identifying your organization’s needs and objectives for hiring a data scientist. Make sure the role requirements align with your organization’s priority initiatives and broader digital transformation goals. Consider questions such as:
What specific skills and expertise are required for this position, particularly in areas like AI, machine learning, and data analytics?
What technical tools, programming languages and frameworks (e.g., Python, Java, TensorFlow, SQL) are essential to success in this role?
What domain knowledge or industry experience would be most valuable to your projects?
What level of experience and educational background is preferred?
How will the data scientist contribute to the strategic goals of your team and the organization?
2. Develop a well-crafted job description
By answering questions like those above, you can then develop a detailed job description that accurately reflects the role and expectations. Taking the time to create a compelling job description can help you attract the right candidates and ensure strong alignment from the start.
In addition to outlining the essentials, highlight what makes your opportunity stand out—such as career growth potential, professional development programs and any flexibility or remote work options you can offer. These remain highly valued by tech professionals when evaluating new opportunities.
Your data scientist job description should include:
A clear overview of the role, responsibilities and objectives
Required qualifications, including technical skills and relevant work experience
Preferred qualifications, such as domain expertise or in-demand certifications
Details about compensation and benefits, including career advancement pathways and training opportunities
Any flexibility or hybrid/remote work arrangements
Information about your company’s corporate culture, mission and values to connect with candidates who share your vision
Need more inspiration? See this example of a data scientist job description from Robert Half, which includes a salary range based on our Salary Guide.
3. Take a strategic and multichannel approach to sourcing talent
As we’ve established, finding and hiring a data scientist is not likely to be quick or simple. Competition for these professionals is fierce, so you’ll need a well-rounded sourcing strategy to connect with the best candidates.
Strategies to consider include:
Utilizing online job boards and industry-specific forums to promote the job opening
Tapping into professional networks and industry events to identify potential candidates, including passive job seekers
Encouraging current team members to refer qualified candidates
Working together with a talent solutions firm to help you locate and hire skilled data science professionals
4. Conduct in-depth interviews to develop a complete picture of candidates
Once you reach the interview stage, go beyond traditional Q&A to get a full view of each candidate’s technical expertise, problem-solving approach and ability to collaborate across teams. Data science roles often require a blend of technical skill and business understanding, so your interview process should reflect both.
To do this effectively, incorporate behavioral interview questions and technical assessments into the interview process and involve key stakeholders from relevant teams to gain diverse perspectives on the candidate’s fit.
5. Consider both technical and nontechnical abilities
While technical proficiency is essential for any data science role, success often hinges on a candidate’s ability to apply their expertise in a business context. The best data scientists can interpret complex datasets and then translate those insights into strategies that support organizational goals.
When evaluating a candidate’s potential to take on a data scientist job, you’ll want to:
Assess their proficiency in programming languages commonly used in data science, such as Python, R, SQL and Java.
Inquire about their familiarity with data manipulation and analysis libraries, such as Pandas, NumPy and SciPy.
Identify other skills relevant to your organization’s tech stack, such as experience with ML frameworks, big data technologies or cloud-based analytics platforms.
As for nontechnical abilities—beyond having an understanding of business—look for candidates who can bring the following skills and attributes to the role:
Strong communication and collaboration skills, including the ability to articulate complex concepts to nontechnical stakeholders
Business acumen to connect data insights to strategic objectives and market realities
Adaptability and a commitment to continuous learning, especially as AI and machine learning tools evolve rapidly
6. Move fast to make a compelling offer
This might be the most important tip of all if you want to hire a data scientist. Tech leaders are already adjusting compensation strategies to move quickly: 59% report increasing starting salaries for AI, machine learning and data science roles compared to a year ago. Competitive pay, along with clear career growth opportunities and a strong onboarding plan, can help you not only land great talent but also keep them.
To speed up your hiring process without sacrificing quality:
Set a specific hiring timeline and share it with interviewers and decision-makers.
Use structured interviews with a consistent rubric to accelerate decisions.
Pre-align decision-makers and pre-approve compensation ranges so you can issue prompt offers.
Partner with a specialized staffing firm to surface vetted candidates quickly.
Stay open to hiring for potential and providing training
Recruiting data scientists requires careful planning, but you can widen your talent pool by considering professionals from adjacent fields and investing in upskilling. Data science is multidisciplinary, and candidates from roles such as software engineering, statistics, business analysis, database administration, and machine learning engineering can often transition successfully with targeted training.
As the need for data science and other skilled technology professionals grows, so will the need to find creative ways to develop technology talent from within an organization to bridge tech and IT skills gaps. One of the recommendations outlined in Building Future-Forward Tech Teams is to create a learning-driven work environment that helps employees prepare to take on new responsibilities in areas like AI.
Robert Half’s head of data science Danti Chen, Ph.D., has some advice for tech leaders who are open to growing their own talent in data science and other disciplines and want to expand the capabilities of all their workers. “To help your tech team acquire new skills, give them projects that require exploration and experimentation,” she says. “Most important, give them room for error.”
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Whether you are looking for a data scientist or other skilled technology talent for your permanent team or to support a special project, Robert Half can help. Learn more about our staffing services for tech and IT today.
