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The maturing role of the data scientist

The role of the data scientist is rapidly evolving and maturing. From a limited specialism operating out of lab environment, data science is going mainstream – sitting at the intersection of business and marketing planning tables as an intrinsic part of the core team.

The evolution of the role is twofold:

  • working knowledge of this stream is becoming a life-skill for domain experts in any business stream,
  • while data scientists are increasingly being asked to answer specific business problems, becoming a key driver of growth.

This is leading to a rapid evolution of the data scientist role, both in terms of the science and application. The technical proficiency is being balanced with business acumen and mastery of statistics is being tampered with dashes of programming and technical skills – making a highly rounded professional than just a geek behind a machine.

Reasons for this rapid evolution can be found in the rapidly evolving environment:

  1. Rise of AI and computing power: A major factor driving this rapid shift is AI and massive computing resources. While earlier data scientists needed to be stats and maths experts to be able to decode the algorithms themselves, these are now being supplemented by highly evolved AI techniques, taking a lot of the focus onto application than development. This is not to suggest that the rigor of stats has become any lesser, it frees up the scientist to think of solution development and take it much further, and faster versus having to spend their energy on the ‘algorithm creation’.
  1. Democratisation of analytics platforms: Once a privilege of large corporate solutions like SAS, SPSS etc. analytics has undergone a dramatic transformation into open-sourced platforms like R and Python. These platforms, nurtured by academicians and community in equal measure has led to a very rapid development of analytics as a discipline. Instead of a handful of developers behind each solution, it’s an unending community which collaboratively furthers the solutions and keeps adding perennial innovation into the analytics development on these platforms.
  1. Rise of collaborative community: Social bug has bitten this community even more than the general public: turning this ‘lab scientist’ environment into a highly social and open collaborative society. Data scientists have their own version of Google or Facebook rolled into one – Stack Overflow – a community which answers each other’s questions, offers new challenges and encourages each other. It’s driving a much rapid growth of the young generation as they find answers and suggestions to their complex problems much earlier in their career. Not just that, it allows them to contribute and add at a much earlier stage in their careers with artificial glass ceilings removed.
  1. Online learning drives it home: The online learning phenomenon has probably affected this stream much more than any other.  Coursera, Udacity have the best in class programs on data and analytics. On one hand this is letting data scientists develop and update their skills at a much more rapid state than ever, on the other hand it is making this life-skill much more accessible to the math-minded domain expert in any discipline like finance, engineering etc.

With technology, learning and application following the Moore’s law, the demands and drive are growing equally in the data science discipline. The scientists are being asked and are wanting to drive the development and deployment aspect in equal measure.

This is most evident in areas like bio-medical sciences where the complexity of problems is tremendously higher than anywhere else and the sanctity of results far more serious as it affects the matters of life and death. These fields are becoming the bedrock of development, and the developments are rapidly spilling over to areas like finance, marketing etc. as rapidly as the tools and solutions become available freely to the whole community and not just a handful of subscribers.

It is also driving a perfect alliance and harmony between the academic research and real-world application, much faster and much stronger. This is transforming the world of a data scientist at an unparalleled speed, bringing it much closer to the mainstream with its tremendous power of information processing.

There will be no ceiling for the data-ready professionals, as long as they stay hungry and well-rounded professionals.

The writer is Harpreet Kaintel, head of analytics and insights APAC, Publicis Media.

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