What are the top qualities, skills and experiences that an organization values in a data scientist that will best aid that individual in a successful data management and analytics career?
Information Management posed that question to members of the Society for Information Management (SIM), to get their take on what makes for a top data professional. Here is select advice from members on what will make a data scientist stand out in the crowd.
Critical thinking and problem-solving
“As you can imagine, a data scientist needs to be a critical thinker – “Why does the business need to know this?”; a linear thinker – “What does the business need to know next?”; and a problem solver,” says Allen E. Look, executive director, global information technology, with the SI Group.
“But as we’ve learned over the years, it’s even more important for data scientists to be curious, to learn continually, and to interact with peers in a congenial and constructive way,” Look says. “The technical skills are quantifiable and easily enhanced through training - without them, the candidate will have no impact.”
“The softer skills ensure a higher level of engagement with projects and people that make a difference - without them the candidate will have no involvement. Taken together, the hard and soft skills ensure a candidate’s success,” Look says.
Must-have skills and nice-to-have experience
“Given that the demand for data scientists presently exceeds the supply, we consider the knowledge and skills candidates must have as well as those we prefer they have,” explains Joseph Bastante, chief technology officer at BlueCross BlueShield North Carolina.
“Must-have knowledge and skills include: data manipulation and processing, programming in common languages such as Python and R, solid understanding of statistics, data visualization, query languages such as SQL, analytical methods and models (e.g., linear and non-linear regression, Bayesian statistics), track record of continuous learning, inquisitiveness, self-directed problem solver, and the ability to navigate and connect with others in the organization,” Bastante explains.
“Preferred skills and knowledge include advanced degrees in data science or related fields of study, deep knowledge of relevant mathematics (e.g., multivariable calculus), past experience with machine learning including neural networks, healthcare industry knowledge, and prior demonstrated successes in data science,” Bastante says.
Data as a decision-driving tool
“Fundamental data science knowledge is certainly the starting point for anyone who wants to pursue it for a career,” notes Jamal Farhat, vice president and chief information officer at BorgWarner.
“Beyond that, it is about developing a solid understanding of how data drives economic value for the sector or company the person is pursuing. This can be in the form of analytics to drive better decision support and insights, or ways that data can be monetized or drive a new business model for the company. As such strategic business savvy becomes an essential pursuit.”
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