Increasingly, data is driving business decision making. Virtually every industry now relies on the analysis and interpretation of data to formulate strategy, streamline operations, and deliver products and services more effectively. But it’s not just about the data itself – it’s about harnessing the power of data science.
Data science is a multidisciplinary field that blends mathematics, statistics, computer programming, advanced analytics, artificial intelligence (AI), machine learning (ML), and subject-matter expertise to develop actionable insights. The professionals who work in the field are in incredibly high demand.
According to the U.S. Bureau of Labor Statistics, employment opportunities for data scientists are expected to grow by 36% between 2023 and 2033, which far exceeds the expected average job growth. For companies that wish to stay competitive and innovative, the ability to attract and retain top data science talent is no longer optional – it’s essential.
What Is Data Science?
The amount of data we create in our daily lives is truly staggering. Harnessing that data can help businesses personalize experiences, improve customer service, make informed decisions, develop new products, and increase sales.
Data science includes several key components:
- ML: Teaching computers to learn from data and make predictions.
- AI: Using machines to mimic human decision-making and problem-solving.
- Data Analytics: Analyzing datasets to uncover patterns and trends.
- Statistical Modeling: Applying statistical techniques to interpret complex data.
In addition to the above, data scientists have expert subject knowledge as well as business and communication skills. Using an interdisciplinary approach that connects these threads, data scientists work with massive datasets, looking for patterns and insights, making predictions, and creating actionable plans.
In addition to data scientists, data analysts, data architects, data engineers, statisticians, database administrators, and business analysts work in the data science field.
A Brief History Of Data Science
The use of data to inform decision making stretches back to the Roman Empire, and perhaps earlier. As explained in A Short History of Big Data:
The ancient Roman military utilized very detailed statistical analysis to ‘predict’ at which border the chance of an enemy insurgency would be the most prevalent. Based on these analyses, they were able to deploy their armies in the most efficient way possible. It is not a far stretch to consider these calculations one of the earliest forms of ‘predictive’ data analysis.
Data analysis has continued to build on those earliest processes. In the mid-20th century, analysts worked with small datasets, using a pencil and paper. But, as the world has moved increasingly online, staggering amounts of data have been created. Managing “big data” requires machine learning algorithms, cloud computing, and advanced storage solutions.
Several computing developments have been key to advancing data science as a discipline. The following milestones have been particularly important:
- The launch of the internet and the proliferation of personal computing devices, particularly smartphones;
- The development of open-source programming languages like Python and R;
- The advent of powerful machine learning frameworks like TensorFlow and PyTorch; and,
- The widespread adoption of cloud platforms that enable massive data storage and processing.
As data science has developed, the use of data to inform business decisions has spread to almost every sector, including government, finance, healthcare, and manufacturing.
Strategizing Data Science Applications
Data science is a broad field that can be applied across almost all sectors in widely diverse ways. Understanding how your business can leverage data science will help in identifying the best candidates for your data science roles. Consider applying data science to the following aspects of your business:
- Anticipating customer needs and improving the customer experience;
- Optimizing your supply chain to reduce costs and inefficiencies;
- Detecting and preventing fraud and suspicious activity; and
- Creating highly targeted and effective marketing campaigns.
We are only beginning to understand how widely data science can be applied to business, and the above are only a few selected examples. But clarifying your strategy for the application of data science will help you attract and select the best candidates for your roles.
Common Technologies, Platforms, And Skills To Prioritize In Data Science Talent
The data scientist role requires an incredibly wide skill set, encompassing specific hard technical skills, well-developed soft skills, and industry-specific knowledge. As you seek top talent to fill your data scientist roles, it’s important to assess the required knowledge and skills while also ensuring that the candidates you hire are a terrific fit for your company and its culture.
Post-Secondary Degree
While there has been a recent movement away from traditional educational requirements in new tech hires, the data scientist role remains an exception. In fact, large numbers of data scientists hold advanced degrees, and that’s a trend that’s expected to continue.
Because data science requires an interdisciplinary and scientific approach to problem solving, a post-secondary degree demonstrates that a candidate has the necessary academic and critical thinking skills to be successful in the role.
While it may seem intuitive to seek candidates with degrees in math, statistics, computer science, or data science, those with backgrounds in other disciplines may be just as competent working with data, if not more so. Many successful data scientists have degrees in diverse subjects, including economics, physics, biology, ecology, anthropology, psychology, philosophy, and political studies. The key – regardless of the discipline – is an emphasis on working with data.
Technical Skills
In addition to a post-secondary degree, data scientists need a wide range of hard skills. Look for candidates who are highly competent in:
- Programming languages such as:
- Python and R (the industry standards for data analysis and machine learning);
- SQL and NoSQL (essential for working with databases); and
- Java and Scala (often used for building large-scale data systems);
- ML and AI tools like TensorFlow, PyTorch, and Scikit-learn;
- Tools for data processing and storage, such as Hadoop, Spark, and Snowflake;
- Visualization tools to present data, such as Tableau, Power BI, Matplotlib, and Seaborn; and,
- Cloud platforms, such as AWS, Azure, or Google Cloud.
Soft Skills
While data scientists need defined technical skills to be successful, well-developed soft skills are just as critical. In some ways, it’s easier for people to learn hard technical skills (which can be taught) than soft skills (which are often inherent but can be learned with consistent practice).
When assessing data science candidates for soft skills, pay particular attention to the following:
- Critical thinking;
- Problem solving;
- Communication; and,
- The ability to explain complex scientific information in non-techical language.
Well-structured behavioral interviews are the best way to assess soft skills in candidates.
Projected Demand For Data Scientists
The IT sector has been plagued by historically low unemployment. Data science is no exception. The U. S. Bureau of Labor Statistics projects job growth for data scientists to be between 25% and 36% over the next six years, significantly higher than many other roles in engineering and technology.
The talent shortage is heightened in data science since the required skill set is broad and deep, combining technical expertise with strategic thinking, subject-specific knowledge, and business and communication abilities.
That demand keeps data scientists’ salaries competitive. According to recent salary surveys, entry-level data scientists can expect to earn between $85,000 and $110,000 annually, while experienced professionals can expect salaries of $150,000 and higher.
Strategies For Attracting And Retaining Data Science Talent
Attracting and retaining top data science talent requires a proactive, thoughtful approach. Competition for skilled data scientists is fierce, and the best candidates are often highly sought after. Developing a strategy to attract and retain top data science talent is critical – and will become increasingly more so as new ways to apply data science emerge.
Build A Strong Employer Brand
Employer branding may seem like a trend or buzz phrase, but brands are critical for attracting and retaining top talent. As Gábor Zöld points out, “You need a compelling value proposition that makes talented [data scientists] want to work for you and the existing ones want to stick around for a long time.”
Whether you actively shape your employer brand or not, you do have one. An employer brand is essentially your reputation as an employer. To ensure that your brand accurately reflects the values of your business and attracts talent that is a good fit, spotlight your company values through your online presence and tell your story authentically.
Foster An Inclusive, Collaborative Environment
Cross-functional collaboration is vital to the work of many data scientists. Fostering a work environment where employees are encouraged to assist and support each other will create a workplace where data scientists thrive.
Additionally, creating an inclusive workplace where diversity is valued will make it easier to tap into the vast but oft overlooked talent pool of BIPOC workers, women, people with disabilities, veterans, and immigrants. Given the demand for data scientists, it will be difficult to fill open roles solely with traditional hires.
Offer Competitive Compensation And Benefits
Because data scientists are in high demand, offering competitive salaries is critical to attract top talent. But in addition to salary ranges, flexible work arrangements, access to continuous learning, availability of cutting-edge tools, and opportunities for career advancement will help to attract the best and brightest data scientists.
Encourage A Healthy Work/Life Balance
Flexible work arrangements – in terms of opportunities for remote work as well as flex time – are highly valued by most professionals today, including data scientists. Offering flexible work arrangements and incorporating values that support a healthy work/life balance can be highly beneficial for both recruitment and retention.
Retain A Recruitment Firm That Specializes in Data Science Talent
Data science is a broad and complex field. It can be difficult for those outside of the field (including in-house recruiters and hiring managers) to fully understand and evaluate the skill set required for data scientist roles.
Partnering with a recruitment agency that specializes in data science talent (or, more broadly, tech talent) can ensure that you have access to and attract top talent that is a good fit for your open roles. These firms usually have deep networks and a thorough understanding of how to recruit for tech specialties.
Specialized recruitment firms can help streamline your hiring process, provide access to top-tier candidates, and assist with identifying passive candidates. Working with a specialized recruiter ensures that you target the best talent available in a competitive market.
