Editor’s Note: This post is by Paul Slezak, Cofounder and CEO of RecruitLoop – the World’s largest marketplace of expert Recruiters and Sourcers available on-demand.
Organisations are finding it equally challenging to find skilled data scientists as they are having difficulty understanding which information sources provide the best input for artificial intelligence driven analysis.
This environment has created a conundrum for artificial intelligence and machine learning vendors. Experts forecast that the data scientist shortage will increase by nearly 30-percent by the year 2020. While colleges and universities are working to close the gap, the need for these talented professionals is outpacing the demand for skilled talent.
Additionally, the role of the data scientist has yet to be clearly defined. This is due, in part, because data scientists’ roles are as unique as the organizations that employ their services.
These firms need specialists who can work with business partners to define goals, effectively collect and organize information and develop the best approaches for evaluating data. Data scientists work with enterprises for the entire lifespan of information analysis and management projects. As a result, these experts must also possess skills in specialized areas such as project management, business processes, information technology (IT) troubleshooting and organizational administration.
Currently, artificial intelligence driven big data analysis is in its early stages. However, the field will eventually grow into maturity, just like previous innovative technologies.
Learn more about the role of the data scientist by checking out the following infographic developed by our friends at the University of California, Riverside’s Online Master’s in Engineering program.
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