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Applied data scientists – A natural evolution for software engineers?

TechGig: While the term ‘applied data science’ has various industry interpretations, especially as data science continues to remain in the realm of research for many organizations, in the current context, the reference is to full-stack data scientists.

At Tesco Bengaluru, we believe the present industry environment in India is just right for software engineers to evolve into full-stack or applied data scientists. This is due to three key factors:

#Factor 1: A comprehensive perspective

While data science requires implementation of knowledge from different domains, applied data science demands understanding of far more disciplines to work with big data from varied sources. Applied data scientists need to evaluate the quality of data coming in, analyse it, filter the data, and ensure a qualitative output. This requires expertise in different subjects and clarity of concepts. A majority of software engineers are equipped with these skills basis years of domain knowledge and exposure to multiple disciplines and subjects.

#Factor 2: Practical understanding

Another important factor that works in favour of software engineers keen to pursue a career as full-stack or applied data scientists is the definite need to make a data solution production ready.

Traditionally, data scientists and software engineers work on the same piece in silos, and often there is no knowledge transfer due to time constraints and other challenges posed by the work environment. However, this results in a situation that several data scientists and software engineers in the industry are all too familiar with – the software engineer ends up modifying the solution put together by the data scientist in order to ensure it performs well in the real environment.

But when software engineers with data science skills emerge as applied data scientists, the real environment understanding they bring to the table can significantly impact the output at all stages and therefore, the end solution. Just as we have full-stack software engineers who work on UI as well as Java, we are looking at a future where applied data scientists will work on creating the data framework as well as implementing it.

Factor #3: Academic advantage

This transformation from software engineering to applied data science makes even more natural sense when we consider the academic background of Indian software engineers. The Indian education system places a strong emphasis on subjects such as maths, which is crucial in the context of data science as compared to software engineering. In most cases, a solid base in STEM makes it relatively easier for professionals in the industry to successfully complete formal trainings in data science.

While several industry players and professionals are acknowledging the merit in this approach, there are many who are taking their time to adopt it. This is primarily due to concerns around data science being a complex subject for most students, and more so for industry professionals with exclusive skill sets. At Tesco Bengaluru, this approach has delivered results for the technology team as well as employees who are eager to take up applied data science.

In the next series, we will talk about the significance of applied data science and how we are leveraging the potential of this technology for greater business benefits. 

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