The data scientist role has become increasingly important for organizations as they shift to data-driven decision making. But recruiting top talent and developing a cohesive team of data scientists is proving difficult for many businesses. This ebook looks at some of the biggest challenges facing companies that are trying to build a data science team.
From the ebook:
Where do data scientists come from? They don’t all have PhDs, and they hail from a wide variety of fields of study, levels of education, and prior jobs. That’s the conclusion of Chris Lindner, a product scientist at Indeed, who has gone through piles of resumes that his firm processes.
Only 20 percent of data scientists have PhDs, he found—and they come from varied backgrounds. “Many come from masters and PhD programs, in fields ranging from astrophysics to zoology,” he said. “Others come from the many new data science graduate programs that universities now offer. And still others come from technology roles, such as software engineering or data analysis.”
A lot of data science professionals were software engineers in their previous jobs, Lindner said. “Many transition to analyst roles, while others hop straight to data science.”
His advice to employers: When seeking data science skills, keep your options open. “If you’re looking for a generalist data scientist, don’t throw out a resume just because the field or degree isn’t what you expect. Data scientists are diverse in their education and background. Although most have an advanced degree in some field, there is no one field that dominates the job market. If you’re having difficulty hiring experienced data scientists or scientists out of academia, consider bringing in individuals from software engineering or data analyst roles, as that is clearly a common pathway to data science.”
Ultimately, the diversity required to move data science—and consequently, artificial intelligence—forward requires a multitude of skills. For AI and data science to be useful to a business, it needs to be a team sport.