January 23rd, 2020
Grandmaster Series: How a Passion for Numbers Turned This Mechanical Engineer into a Kaggle GrandmasterRSS Share Category: AutoML, Community, Company, Data Science, Driverless AI, Kaggle, Makers, NLP
By: Parul Pandey
In conversation with Sudalai Rajkumar: A Kaggle Double Grandmaster and a Data Scientist at H2O.ai
It is rightly said that one should never seek praise. Instead, let the effort speak for itself. One of the essential traits of successful people is to never brag about their success but instead keep learning along the way. In the data science world, a name that resonates when we speak of humility is that of Sudalai Rajkumar, who is as famous for his humble nature as he is for his analytical prowess. It is indeed a privilege and an absolute honor to be working with him as a colleague and learning new things every day.
In this edition of the interviews, where I bring to light the journeys of successful data scientists, I shall be sharing my interaction with Sudalai Rajkumar, aka SRK, a Kaggle Competitions and Kernels Grandmaster, and a data scientist at H2O.ai. Sudalai completed his Engineering degree at PSG College of Technology and then went on to earn an executive degree in Business Analytics and Intelligence from the Indian Institute of Management-Bangalore.
SRK brings along with himself a decade of experience in machine learning and data science. He has a large following both in India and abroad, and is a massive inspiration for aspiring data scientists around the world. Apart from getting high ranks in several competitions on Kaggle, SRK is famous for his in-depth kernels too. In fact, he is the former No 1 in the Kernels section of Kaggle.
SRK announced the completion of a decade in the data science industry with a beautiful gratitude note on LinkedIn. Therefore, what better time than this to speak to the man himself about his journey into data science and his advice for the new entrants in this field.
Below is an excerpt from my conversation with Sudalai:
You have a background in Mechanical Engineering. How did the transition to software engineering happen?
SRK: When I finished my degree, I had two job offers — one in a well known mechanical engineering firm and the other in a Startup analytics firm. The mechanical engineering offer was a dream one for me, just like for any other fresh mechanical engineering graduate. However, the joining date was about four months away from graduation, and so I decided to take the other offer.
Initially, my idea was to join the analytics firm to understand more about the company and the nature of work since their interview process was very interesting. In the process, I got intrigued entirely by finding patterns in the data even though I was completely new to software engineering. This passion of mine for numbers made me continue that job and looking back today, I am extremely pleased that I made that decision.
How did your tryst with Kaggle begin, and what kept you motivated throughout your grandmaster’s journey?
SRK: Being from mechanical engineering, I had no formal education in software engineering or Data Science. Hence, I started taking up MOOCs to learn about the concepts. I came across algorithms like Random Forest, SVM, etc. in these courses but did not see anyone using them in the job. This made me look for avenues to experiment with these new algorithms to understand them better. That is how I stumbled upon Kaggle and started my Kaggle journey.
I would say Kaggle is also an addiction once we start doing it, and I am no exception to that. The addiction to build better models and get better ranks sometimes gets a hold on you. There were several failures in multiple competitions, but obsession and passion got me going. Of course, it took a lot of personal time after office hours, but there was immense learning along the way.