Yes, a good data scientist must have a solid statistical foundation, coding experience in Python or R, data management skills utilizing SQL and, preferably, previous industry experience in the space they are applying. But, what makes a great data scientist? Intrinsic characteristics are what differentiates a great data scientist from a mediocre one.
A great data scientist often has contradictory skill sets. They need the intelligence and training to do the data processing and create useful models, but the intuitive understanding of the business problem they are trying to solve and how the structure or nuance of the data and the models serve that need is almost more important. Lee Barnes, head of Pyatronix Data Insights, says, “Without understanding why and how it works, it’s hard to have a lot of confidence in their models. Someone with this deeper understanding and intuition for what they are doing is a true data science whiz, and will likely have a successful career in this field.”
The ever-changing field of data science requires someone who is excited and interested in the new techniques and applications that come out every year. A great data scientist is excited by the opportunity to address their business problems in new and exciting ways. Data Scope Analytics in their blog on the Six Qualities of a Great Data Scientist said, “masters of curiosity are quick to question their own assumptions.”
“I can tell right away if the candidate I have on the phone is a great communicator”, says Data Science and Analytics Executive Recruiter, Paul Chatlos. “Some try to blow me away with their expertise. The best communicators can clearly and concisely explain what they do, how they do it and what it means to the business. A great data scientist is a great storyteller. They can tell a compelling narrative that is understood by everyone.”
Nothing in technology today is done in a vacuum. Gone are the back office jobs that never interacted with senior management or marketing. Integration between the business unit with the need, and systems, applications, data and a great data scientist requires the distillation of challenging technical information into a form, written, visual or oral, that is easy to understand yet comprehensive.
The right solution for the right business need takes patience and determination. Data Scope Analytics calls it grit, “Technologies that appear and then vanish, messy data that can’t be made to ‘fit’, dead ends, wrong turns, roadblocks, red tape, teams full of mixed agendas and personalities, budgets and deadlines” all conspire against the great data scientist to answer critical business questions. The best let go of perfectionism and find a way through the muck to the magic of great analytics.
Interested in hiring a great data scientist? Want to talk about your career in data science? Contact Smith Hanley Associates’ Data Science and Analytics Executive Recruiter, Paul Chatlos at email@example.com.