Data Scientists are often described in terms of hard skills such as math and statistics and the use of a numbers-based approach that requires every decision to be backed by data. But hard skills are only part of the picture of what a data scientist does, and the skills they need to achieve success.
Unlike engineers in decades past, data scientists aren’t focused solely on creating machines that are controllable and consistent. While that remains a part of the equation, their work overlaps with the changing needs of users.
That means both “hard skills” and “soft skills” are essential parts of a data scientist’s job.
Hard Skills for a Data Scientist
It’s impossible to understand the importance of soft skills without touching on what data scientists strive to accomplish with hard skills. These skills include expertise in programming, analysis and data modeling. Organizations collect data while data scientists take it and create programs to find important trends and extract actionable data.
Quantitative analysis specifically requires a data scientist to model complex economic systems, create experimental designs to test a hypothesis and find opportunities to implement machine learning in data extraction and analysis.
A data scientist’s hard skills are supported by a thorough understanding of the product or service the organization provides, as well as the process by which it is created and delivered. Fundamental business skills, or at the very least a detailed understanding of the industry in which they work, are key to success.
Essentially, a data scientist provides the knowledge and skills that make the difference between having large amounts of data and knowing what to do with it. That’s especially true for those who have attained a master’s degree in the field.
Soft Skills for a Data Scientist
Data scientists work in an area that is often not completely understood by executives.
While organizational leaders may understand the value of data science, they are often unclear on all the ways it can support meeting business goals and creating strategy. Soft skills are valuable to data scientists if they want to leverage their substantial hard skills and help business leaders make sense of data.
Communication is a catch-all term that covers a lot of ground. For data scientists, effective communication skills can come into play in situations such as:
Explaining value. Educating executives on the value of data science and the opportunities to improve business through data-driven decisions.
Explaining insights. This is the other end of the process. Once a project is completed, it’s the responsibility of the data scientist to present findings that are easily understood by those outside of the field. It’s also important to clearly communicate recommendations that are realistic and actionable. People speak of “disruptive” technology as if it’s a given. But making a transition to data-driven strategies can prove a difficult process in companies used to legacy systems. The ability to explain the insights and possibilities found in data is critical to this process.
Data Visualization. In both the above scenarios, data visualization is important. The majority of people understand complex issues better when visual and words are coupled together. Mastering this type of presentation can become one of the most important soft skills.
Teamwork is essential in data science. The most successful data scientists know they reach the most significant findings when getting feedback on their work. A data scientist also needs an in-depth understanding of the industry in which they work. That requires collaborating with people in other departments who do not speak in the same daily language of data and development. But forming those partnerships is vital to understanding how a data scientist’s individual contributions fit into the overall operation.
And among data scientists, it’s important to share knowledge. Data science is a vast, complex and relatively new field. The sharing of techniques and tools is important to success.
As data science becomes a central component of many organizations, these soft skills will become more valuable. While expertise in hard skills is at the core of the job, soft skills can raise the role of a data scientist to a new level.