Demand & Supply
Data Science jobs have been on the rise but the domain expertise required to fill these openings, have not been able to keep pace with it. This is in part, because data science, as a field, is rapidly evolving and the existing frameworks haven’t completely matured yet. In such a dynamic environment, it helps if the job roles are clearly demarcated if we stand a chance to bridge the chasm between demand and supply. this standardization in the job role description can only be achieved if we are clear about the business requirements and our expectations from the said roles. But that is easier said than done, various impediment thwart our ability to streamline this process.
The Perception Issue
- From the business standpoint, roles like data analyst are relatively well understood but just as we delve a little deeper into the technical qualification of the data engineer, the lines become a bit blurred
Steep Learning Curve
- Due to the vast nature of its domain, it is not possible to cover a multitude of subject matter, in a short time. This in turn influences our competency and area of specialization in the field. In other sectors, there is usually a single strong-point which defines the job description, but in the data science domain, mastery over a plethora of technologies might be required and any difference might lead to discrepancy in the job roles.
Choosing the Correct Role and Skill
- At the end of the day, businesses post job openings based on what they require. Notwithstanding the semantics of the Job title, we must approach it from the skill and even specifically the technology stack point of view.
- By: Yogesh Kumar Das