Organizations often engaged in conversations, where the role and deliverance by the data scientists are scrutinised under the lens of uncertainty. Often the topic about the necessity of chief data scientists is discussed amongst organizations. What most organizations fail to comprehend is, the profile of chief data scientist is not confined to work as an employee in the organization, but they unburden the Chief Technology Officers’ (CTO’s) job, by monitoring Data scientists.

Data scientists are one of the most valuable entities of an organization. They are the modern-day, data-hungry miners of the tech world, who can convert the data coal into valuable insights. They are researchers who explore every option, look at every algorithm before giving a green flag for the insights.

Truthfully, in traditional organizations, the possibility of data scientists getting the required amount of guidance and monitoring by CTO becomes less. Ira Cohen, CTO of Anodot says, “The reason why you need a Chief Data Scientist in the first place is you need somebody who can bridge the gap between management and [the data scientists], and what machine learning can do and cannot do. You need somebody who understands what it is in a deeper form than a CTO, who might have a broader knowledge of a lot of things, but not necessarily machine learning.”

The machine learning algorithm is backed by a huge amount of data. But the journey from harnessing data, deploying algorithms, and gaining valuable insights, is not exactly a smooth sale. Different departments have silos, which thwarts the trusts amongst different organizations. The outcomes are often not exactly according to people’s expectation. A part of chief data scientist’s job is to make sure that machine learning models are working well, and that data is transferred seamlessly.

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