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How Far Can Kaggle Help You In Your Professional Career?

How Far Can Kaggle Help You In Your Professional Career?


Any data scientist worth his salt would be familiar with Kaggle. The platform has emerged as an ideal medium for data science practitioners to use and sharpen their skills as they navigate this dynamic field. In fact, its popularity has also caught the attention of recruiters who have begun to use Kaggle achievements as one of many metrics to gauge the competitiveness of candidates.

But how far can Kaggle help data scientists in their professional careers? We reached out to some who weighed in with their thoughts:

Value of Kaggle Rankings

Having a good ranking under the ‘Competitions Category’ on the platform can greatly help in the career advancement of participants. Not only does it demonstrate their skills, but also offers a window into other qualities they may possess, including time management, persistence, as well as consistency.

“To achieve a good ranking on Kaggle, individuals need to persistently dedicate several hours of time over a period,” says Usha Rengaraju, a data science consultant, who is incidentally India’s first female Kaggle Grandmaster. “Dedication, consistency, innovation, out-of-the-box thinking, ability to not give up and collaboration – all these are key traits of a top kaggler, and they are also the key skills which any employer may look for,” he adds.

According to her, although Kaggle problems and real-world business issues can be seemingly different for certain domains, competition ranking serves as a better metric to evaluate candidates on their data science abilities.


“There are stories of several Grandmasters – some of them with no structural education – landing up high profile jobs,” she adds. Chimes in Prashant Kikani, who works at a Bengaluru-based startup, “This is especially relevant for amateur data scientists and allows them to gain an edge over others. What is more, in order to get a higher ranking, one must be in the know of what is latest in terms of research, Kaggle can not only augment your job profile but also make you more informed.”

ALSO READ: How To Initiate Your Kaggle Journey

Wide Exposure

Although Kaggle is known to fine-tune a data scientist’s skills, it is also a great platform to learn from seasoned data science professionals as well. Although senior data science professionals are largely missing from the platform, the ones who are active can provide a wealth of information to aspiring data scientists.

“There are some very great kernels prepared by seasoned AI engineers,” says Saurabh Jha, Director – Data Science at Dell. “Discussion forums are helpful too. It gives an exposure to all kinds of problems – from Computer Vision, Text, Recommendation Engine, etc,” he adds.

These problems, although different from real-world issues, help push the boundaries of a user’s abilities, as some take a lot of time to solve and demand a lot of collaboration with other kagglers. In a world where employers are relying on non-traditional hiring methods, the wide exposure that Kaggle affords makes its credentials worth a lot to recruiters.

“Another important aspect is that candidates learn the art of critical thinking,” says Vidhya Veeraraghavan, Head of Analytics – Financial Markets Operations at Standard Chartered. “Apart from the programming skills and implementation of ML algorithms, candidates can also learn to approach the problem statements and the datasets with multiple solutions,” she adds.

See Also

ALSO READ: How To Find Success In Kaggle – What Masters Recommend

True Demonstration of Working Knowledge

Although the surge in data science courses has enabled enthusiasts to pick up critical skills, only a few of these learners have opportunities to work on real-life projects. This is where platforms like Kaggle help fill the gap.

“To become job-ready or excel in their data science jobs, candidates need to have some hands-on experience, and Kaggle allows just that,” says Vidhya.

She illustrates this with an example: “Consider a three-tiered Pyramid concept. Acquired knowledge occupies the bottom-most tier, working knowledge will be in the middle, followed by domain knowledge at the top-most tier. In my opinion, to have a professional career in data science, a candidate’s working knowledge, that is, the middle tier, is imperative.”

Since Kaggle helps candidates work on real datasets and compete with other elite minds, it gives them the opportunity to sharpen and hone the ‘middle tier’.“Candidates can test their knowledge in the basics of programming languages, machine learning algorithms and its implementation too,” says Vidhya. “This definitely gives a boost to their resume and if articulated well, can help them ace their job interviews,” she adds.

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