The amount of data generated globally is growing exponentially due to digital transformation initiatives, the proliferation of Internet of Things (IoT) devices, social media activity, e-commerce transactions and more. According to Statista, global data creation is projected to grow to more than 180 zettabytes — or 1 billion terabytes — by 2025.
That’s a lot of data. And the speed at which data today is generated and needs to be processed requires real-time or near-real-time analysis capabilities — and the specialized skills of data scientists.
A side effect of the increased demand for data science talent is a short supply of these professionals in the labor market. That is leading to critical skills gaps in many organizations. U.S. technology managers at businesses of all sizes surveyed for Robert Half’s e-book, Building Future-Forward Tech Teams, cited data science among the areas where skills gaps are most evident in their departments.
While the current artificial intelligence (AI) boom, including the rapid rise of generative AI systems and tools, is helping to fuel the need for data scientists, the skill sets of these technology professionals are vital for supporting data analytics and innovation projects for almost every department in a business, including marketing, sales, finance, operations and product development.
Data scientists apply sophisticated analysis techniques to extract insights from vast amounts of data in various forms — from structured data like online spreadsheets to unstructured data such as social media analytics — for businesses to use in decision making and to drive strategic initiatives.
These insights can help companies to, among many other things, optimize processes and improve efficiency, reduce costs, inspire the development of new products and services, manage risk, uncover new market opportunities, and personalize customer experiences.
Given the complex and data-intensive nature of their work, data scientists need strong analytical, data mining, quantitative analysis and multivariate statistical modeling skills. The latter term refers to techniques for understanding relationships between variables and extracting underlying patterns in complex data sets. The approach is used in predictive modeling and many other applications.
Experience in using programming languages like Python or Java and writing SQL queries for data manipulation is also typically required for data science roles. Typical responsibilities for a data scientist include:
Presenting complex analyses as action-oriented recommendationsManaging and merging data from multiple sources and generating reportsReviewing applications, data sets and models for anomalies to help ensure accuracyApplying machine learning algorithms to large-scale data for predictive modeling
Nontechnical skills are crucial for data scientists to possess as well because they need to work within, or with, various functions in an organization beyond the IT department. Data scientists with a holistic understanding of business are particularly valuable contributors to AI projects.
In Building Future-Forward Tech Teams, Robert Half’s head of data science Danti Chen, Ph.D., notes that she seeks professionals for her team who can apply a business lens to their work. She explains why this is important: “You can have a very solid programmer, but if that person doesn’t think about the real-world implications of data, they won’t likely do a good job of cleaning the data to remove the noise or doing the necessary gut checks to make sure the signals make sense.”
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. And 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.
That means recruiting data scientists, who are vital to supporting AI and ML projects, isn’t going to get easier anytime soon. So, you’ll want to have a strong game plan going into your search for this talent. The following six steps can help you take a structured approach to the process so you can compete effectively for these in-demand professionals.
First, clearly identify your organization’s needs and objectives for hiring a data scientist by considering questions such as:
What specific skills and expertise are required for this position?
What technical tools and programming languages are essential to success in this role?
What domain knowledge or industry experience is ideal for a candidate to possess?
What level of experience and educational background is preferred?
How will the data scientist contribute to the strategic goals of our team and the broader business?
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 is a must if you want to attract the best available candidates in a competitive hiring market. It 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, and opportunities for professional growth
Also, be sure to provide information about your company’s corporate culture, mission and values so that your job description will be more likely to catch the eye of candidates who align with your organization’s ethos.
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.
As we’ve established, finding a data scientist available for hire is not likely to be quick or easy. So, you’ll want to cast a wide net and leverage multiple channels to increase the likelihood of finding the best candidate for the role, and for your team. Strategies to consider include:
Utilizing online job boards and industry-specific forums to promote the job opening
Tapping into professional networks, alumni associations and industry events to identify potential candidates, including passive job seekers
Encouraging employee referrals and incentivizing current team members to recommend qualified candidates
Working together with a talent solutions firm to help you locate and hire skilled data science talent
Once you reach the interview process, you’ll have the opportunity to assess a candidate’s ability to succeed in the role and thrive in your organization. So, you’ll want to cover a range of topics, including the potential hire’s problem-solving abilities, past experiences and enthusiasm for continuous learning.
Be sure to use behavioral interview questions to gauge how a candidate has handled challenges and achieved results in previous roles. You may also want to include practical exercises to evaluate a professional’s ability to apply their skills to real-world scenarios. Also, consider involving key stakeholders from relevant teams and other functions in the interview process to gain diverse perspectives.
Technical proficiency is a cornerstone of any data science role, of course. 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 JavaInquire about their familiarity with data manipulation and analysis libraries, such as Pandas, NumPy and SciPyTest problem-solving abilities and critical-thinking skills through coding challenges, case studies or technical interviewsIdentify other skills relevant to your organization’s tech stack, such as experience with ML frameworks or big data technologies
As for nontechnical abilities — beyond having a holistic understanding of business — look for candidates who can bring the following skills and attributes to the role:
Strong communication skills, including the ability to articulate complex concepts to nontechnical stakeholdersA collaborative mindset and the ability to work effectively in cross-functional teamsAdaptability, and a willingness to learn new technologies and methodologiesAnalytical thinking and attention to detail in problem-solving
This might be the most important tip of all if you want to hire a data scientist. If you’re confident you have found a solid candidate for your team, don’t wait long to offer that person the job.
Moving too slow could mean missing out. In a Robert Half survey, nearly half (48%) of technology hiring managers reported that hiring quickly enough to land the best talent is one of their greatest hiring challenges.
Recruiting data scientists requires careful planning, strategic sourcing and rigorous evaluation to find the best hires for your needs. An openness to recruiting trainable talent can also help you staff crucial roles.
Because the field of data science is multidisciplinary, professionals with various technology backgrounds can become successful data scientists. Those pros include:
Software engineers and developers with proficiency in relevant programming languages, experience in developing algorithms, and familiarity with data structures and databases
Statisticians and mathematicians with a strong foundation in statistical methods and probability, which are core to data analysis and modeling
Business analysts and data analysts with experience interpreting data, providing actionable insights, and creating visual representations of data to communicate findings to stakeholders
Database administrators and data engineers who are highly skilled at managing and querying large databases, and designing and implementing ETL (Extract, Transform, Load) processes for preparing data (knowledge of big data frameworks like Hadoop and Spark is also a plus)
ML engineers and AI specialists with in-depth knowledge of ML algorithms and frameworks like TensorFlow and PyTorch, and experience in deploying ML models into production environments
Other professionals who could be trained for a data science role, depending on the position's requirements, include research scientists, academics, IT and network specialists.
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 continuous cycle of learning that helps employees prepare to take on new responsibilities in areas like AI.
Robert Half’s Danti Chen 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.”
