Demand has consistently been on the rise for data science roles and analytics skills across all industries. In 2021, it’s predicted that there will be an additional 3,037,809 new job openings in data science worldwide as companies move to become data-driven.Whether you’re an aspiring data scientist yourself or just looking to acquire the mindset of one, knowing the essential qualities it takes to succeed in the role can help highlight what to focus on in your development. This leads us to the question: What does it take to be a successful data scientist? Here are the traits that set effective data scientists apart from the rest.Business VisionThe most successful data scientists commonly have the ability to understand the company’s situation from a business standpoint and always keep the organization’s overarching goals in mind. This is important to understand the why behind the data. Business acumen is the key to determining what critical business questions the data is looking to answer or what questions need to be asked.Being a data scientist isn’t solely about writing code and developing data models. General knowledge of organizational goals and challenges can help you to start asking the right questions and develop useful queries.
Analytical Reasoning
Due to the technical nature of the position, possessing analytical and critical thinking skills is necessary for success. Working with data is all about identifying patterns and thinking quantitatively. Data scientists need to be able to look at any particular problem objectively in order to come to logical conclusions.Scientists should also be considering different angles and perspectives when looking at their data. Constantly asking questions and deriving insights from various points of view are crucial to driving effective and objective analysis.
Curiosity
A data scientist's abilities should extend far beyond their technical expertise. Soft skills such as curiosity and creativity are what distinguish good scientists from great ones. The job title contains the word ‘scientist’ for a reason, not all of the answers are known! You will need to theorize, develop hypotheses, experiment, and ultimately draw conclusions on a day to day basis. Curiosity is needed when it comes to handling these tasks and diving deeper into the complex problems at hand. Great scientists should take an iterative approach to understand their data and be open to questioning their initial assumptions. Highly effective data scientists are always looking for the “why” and “how” behind the data in order to probe for additional information. Exploration and experimentation are vital to producing conclusive insights.
Communication
Furthermore, another essential skill for data scientists is communication. One of the primary responsibilities of a data scientist is to communicate their methods and findings to other business units. With the abundance of data today, it can be messy and difficult to understand, especially if you are entirely unfamiliar with the data. It’s important to communicate insights in a way that's easy for others to understand and drive decisions from. After all, what good is the data if no one can understand it? Any skills surrounding storytelling abilities or communication will help to properly inform key stakeholders on their queries.
Collaboration
Whether you are working within your department or with others to collect and communicate data, collaboration is critical. Much like every job, teamwork plays a vital role in maximizing productivity. This working relationship, though, is especially important for data scientists as there is often a disconnect between business units and data science teams. Data scientists help to bridge the gap between the two business functions and allow for greater effectiveness all around.
Conclusion
Overall, many traits can contribute to the success of a data scientist. But it’s important to note that none of these traits are necessarily required or set in stone when it comes to the makings of an effective one. The role contains a number of unique responsibilities but there remains an opportunity to make it entirely your own. Consider these traits as a common data scientist’s keys or guide to data mastery when developing in their career.
