Businesses across the world are hiring data scientists to beef up their efficiency and competitiveness via artificial intelligence (AI). Startup companies (dubbed AI-First companies) are disrupting traditional industries like banking, insurance, real estate and healthcare using AI technologies.
The demand for data scientists far exceeds supply. And, the problem is exacerbated by the fact that the data scientist profession is itself splitting into multiple sub-disciplines. Some examples of this divide include: Decision scientists have domain expertise and specialize in linking the domain knowledge and the algorithm to solve a problem. Data scientists have expertise with machine learning (ML) and related algorithmic fields at the application level, i.e., they know how to apply algorithms to data sets to generate successful experimental insights.