The field of data science and machine learning is emerging as one of the rapidly-growing and most sought-after field today. Data has turned out to be the most valuable asset for every firm in every industry. Companies have begun to invest in resources and technologies in order to capitalize on the business potential buried in data. As a result, the market for data scientists has seen a significant growth in demand.
While individuals are pursuing a data scientist career by honing their skills in order to gain market share, businesses are unsure about the requisite skill set. This is because the field is new to the top management, and they are just caught up in the buzz. The majority of them are naturally considering hiring a statistician with a PhD in statistics, which is not necessary.
If the objective at hand is to do research in complex statistical models and algorithms, hiring a PhD makes sense. With the abundance of off-the-shelf modeling tools and technologies on the market, 99 percent of the time, all that is required is a well-equipped resource who is experienced in the most up-to-date tools available and has a good understanding of statistical models and methods. All of the models and algorithms have been pre-defined by statisticians and numerical scientists and are available in any data science toolkit on the market, like Python, R, Octave, and Matlab etc. At the end of the day, it all boils down to knowing when to use which tool upon what data to provide business value for any company.