3 top soft skills needed by today’s data scientists
Data science skills are in hot demand. Companies want people who can turn big data into actionable insights and develop predictive models with artificial intelligence.
Statistics from Indeed show that since 2013, data scientist job postings have grown a whopping 256 percent, with a 31 percent jump in year-over-year postings as recent as December 2018.
With the demand for data scientists running high, software providers have been working to create new technologies to democratize these skills and empower other business users. These “citizen data scientists” can then perform some of the analytical tasks once reserved for data experts.
As these tools become more sophisticated by the day, how can trained data professionals and specialists remain relevant and at the top of their field? The answer lies in having soft skills to interoperate with different industries, technologies and people.
Skill #1: “Bilingual” understanding of domain and data
One of the reasons why companies want to create citizen data scientists—beyond filling in a talent gap—is that they can combine their existing domain knowledge with data and analytics to solve critical business problems.
This intersection of business and data is where the most exciting opportunities for data science are. From law and medicine, to retail and archaeology, practically every business and industry can benefit with new discoveries and smarter decisions by way of data.
Data scientists who can understand the business context, plus the technical side of the equation, will be invaluable. This kind of “bilingual” talent can turn data streams into a predictive model, and then translate that model into a working reality, such as for financial forecasting. Core skills in storytelling, problem solving, agile development, and design thinking are critical to interoperating within different business contexts as well.
The key is to develop T-shaped skillsets, as opposed to being I-shaped. While I-shaped people have a deep, narrow understanding of one area (like data engineering or data science), T-shaped people have both in-depth knowledge in one area and a breadth of understanding of several others. It is easier for T-shaped people to meld their data expertise to a broad range of use cases and industries.
Skill #2: The ability to continuously learn
Technical knowledge, such as in Python and R, is currently very important to data science. But in the fast paced world of technology, it is hard to gauge if these programming languages will be around in two or five years’ time. To stay relevant for years to come, data scientists should have a constant curiosity and ability to learn new skills.
One problem is most academic institutions, as well as corporate learning and development programs, focus on cementing skillsets in traditional teacher-student environments. These are not conducive to continuous learning in face of technological change.
At Genpact, our Genome reskilling initiative leverages the collective intelligence of knowledgeable employees to reskill groups of people together.
Our approach, built on work with the Massachusetts Institute of Technology’s Center for Collective Intelligence, centers on the fact that there are often already people within an organization with understanding of new technologies who can crystallize, contextualize, propagate and continuously update that knowledge for others. After all, people learn better from each other and by doing, with context to their real-world work.
By concentrating on the collective—not individual—intelligence, we are able to create nimbler workforces that can learn and adapt to a quickly changing world, almost like a collective brain would – or in MIT’s parlance, a “supermind.” Similarly, young data scientists who have recently graduated should start thinking about how they will learn outside of the classroom environment and continue to upgrade their skills with the latest and greatest.
Skill #3: Fundamentals of human behavior and ethics
Technology is constantly changing, but human nature and behavior are not. It is useful for data scientists to understand the basic elements of behavioral economics and communication. Even the most astute data scientist will have a hard time understanding a dataset and relating it to how people function and act without the fundamentals of human behavior.
The communication side will be especially important as data expertise gets pulled into interdisciplinary use cases. Data scientists will have to be able to talk to people with different backgrounds. This goes back to the need to be more T-shaped to effectively translate highly technical ideas to different business contexts.
Furthermore, a sense of morality and ethics will be necessary as data continues fueling AI algorithms that impact people’s personal and professional lives. Ethical use will be a matter of protecting not just consumers, but also company reputation. Data scientists who can look at a model through an ethical lens to find questionable applications and lack of diversity within data samples (which lead to bias) will prove to be vital.
While the demand for data expertise continues to grow and new tools advance, it is up to each data scientist to keep up their skillsets and prevent their talents from becoming outdated. Those that continue to learn and evolve with the times—and can blend industry and technology, among other critical soft skills—will enjoy future-proof careers and excel in the long run, no matter what changes are on the horizon.