Is Kaggle Useful in Finding a Machine Learning Job?

If you are in any way connected to the tech industry, chances are that you have heard of Machine Learning! It’s the current cutting edge technology that has a wide range of applications in almost all sectors. And if you have heard of Machine Learning, chances are that you may be interested in learning more about it and enhancing your knowledge. Kaggle is currently the best platform to meet the machine learning and data science community and learn more about this fascinating technology. It’s the largest platform for machine learning in the world with more than 23,000 public datasets for practicing and different competitions to enhance your skills. Some of these competitions also pay an insane amount of prize money (1.5 Million was offered once!).

But the question still remains…. Is Kaggle useful in finding a machine learning job? Sure, you can learn about machine learning from this platform as they provide free courses and also implement your knowledge in different competitions but is this industry-relevant in any way? And will practicing and creating a presence on Kaggle help in job interviews and bagging a Machine Learning job?

How are Companies finding Machine Learning Talent using Kaggle?

Machine Learning is a relatively new technology which means that there are many paths to becoming a Machine Learning Engineer. Most of the people working in these jobs don’t have a formal education in machine learning or data science but in varied fields like computer science, statistics, business management, or even psychology! This obviously means that companies also use unorthodox methods for recruiting machine learning talent as well.

One of the paths for this is Kaggle Competitions. A plus point for these competitions is that they use real-world data that is mostly provided by the organizations hosting these competitions. For example, Kaggle currently has competitions like predicting student knowledge over time, predicting quantitative trading, etc. which offer prize money of $100,00.

This basically means that companies use Kaggle competitions as a way of finding out the different solutions to a problem. These solutions are created by people all over the world with different academic and industry backgrounds which only provides a deeper exposure to machine learning. Another advantage of taking part in these Kaggle competitions is that they are a great booster for your resume. Having excellent and consistent performance in competitions can be a plus point while applying for an internship or job in machine learning and even get you the coveted position. A great example of this is Gilberto Titeric, who was the number one ranked on Kaggle in 2015 and bagged a job at Airbnb with offers from Tesla and Google as well! Some companies even specify in their job requirements that having experience of winning Kaggle competitions would be a plus point in the hiring process!

There are even more direct ways of getting job opportunities from Kaggle competitions. There are many companies that specifically create competitions where the winners get an opportunity for an interview with their machine learning team. Some popular companies that had Kaggle competitions for hiring include Facebook, Airbnb, Yelp, Telstra, Walmart, etc.  One of the previous winners of Facebook’s Recruiting competition on Kaggle, Tom Van de Wiele, even got the opportunity to change his career from Eastman Chemical Belgium to DeepMind, an artificial intelligence company owned by Alphabet which is Google’s parent company! So it’s obvious that performing well in Kaggle competitions opens up a lot of doors for aspiring machine learning engineers or those who wish for a career change.

Some Opportunities Not Provided Just by Kaggle

While Kaggle can open a doorway to getting a job in machine learning or data science, it has some disadvantages that make it only part of the hiring process. This means that your job application cannot be contingent on only your Kaggle profile. One of the disadvantages of Kaggle is that participants only use cleaned and curated data that is provided by the organizations. They are also provided a clear-cut question for which they need to find an answer from the data. This does not simulate real-world problems where the data is often dirty and complicated and there is no fixed question to be solved using machine learning. Since Kaggle does not provide experience on this aspect which is critical for a job in machine learning, it’s important to do some independent projects as well that highlight your capabilities to recruiters.

A machine learning career also requires business acumen to understand how the data will fit into the company’s profile and enhance its profitability. It’s equally important to have excellent communication skills so that complex machine learning and data science concepts can be explained to decision-makers that are not necessarily from technical backgrounds. Kaggle does not provide any opportunities to enhance these skills which are also an essential part of getting a job as a Machine Learning engineer. To overcome this flaw, you can apply for machine learning internships that provide a broad understanding of becoming a machine learning engineer.

So Will Kaggle Help You Get a job?

All in all, Kaggle is a very useful tool in finding a machine learning job. An excellent Kaggle profile will definitely result in a lot of exposure from recruiters which will help you in getting a job! Performing well on Kaggle demonstrates problem-solving skills and teamwork, which are some characteristics necessary for becoming a good machine learning engineer and help you in standing apart from the crowd. However, it’s important to remember that while Kaggle is definitely helpful in getting a Machine Learning or Data Science job, it’s not the only factor to consider and is only a part of the recruitment process. Unless you are really exceptional and a Kaggle grandmaster or something!!! In essence, it’s a great idea to join Kaggle and take part in competitions, but you should also remember to work on some independent projects and an internship if possible.