[levanify]

Awesome post! Thank you for taking the time to document down your learnings along your journey of pursuing data science. What I really like about your journey is how one thing manages to lead to another in the most amazing way. Some people attribute it to luck, but I guess in your case you really have made your own luck by taking actions to move towards your goal! Congratulations on your accomplishments thus far! I just have a few questions which would love if you can elaborate further on.

Firstly, it seems to me that the online course enabled you to reach top 3% of a Kaggle project which led to a series of career opportunities. However, I believe that being able to reach the top in Kaggle competition is not an easy feat. This kind of process is not really replicable especially for the people who are just starting out in Data Science. In that case, what would you recommend them to do?

And secondly, what is the end goal of a data scientist or at least for you as a data scientist? Would it be going into research? Continue to climb the ladder till you reach the top of the company? Data science and engineering in general are great as a form of intellectual challenge. However, personally sometimes I feel that there is a lack of meaning in the thing that I am doing as engineer/data scientist (especially if the culture of the company is very bottom-line driven). Just want to hear your thoughts! :)

[7d7n]

On the first point: I assume you're asking for avenues to practice and demo your work. If so, personal projects are a great way.

The most impressive candidates can demo something they've deployed. This demonstrates the ability to apply what you've learned AND learn the rest of the stuff as needed (e.g., spin up EC2, build basic front end, maintenance, etc.). The latter is more important and gives confidence that you take ownership and likely can do the same (i.e., end-to-end with results) in the company.

On the second point: having meaning in work differs from person to person. Some people enjoy pushing the envelope (research), others enjoy staying hands-on (individual contributor, IC), while some big picture thinkers enjoy coordinating (PMs). It's helpful to think back on what gave you the greatest satisfaction and work with your leaders to be a role that lets you perform your best.

I find it most fulfilling to build data products that are useful, relative to generating tons of revenue. I would be equally happy as an IC doing the hands-on or leading a small team and mentoring them to become more effective.

[visarga]

I have interviewed many applicants to the ML engineer position. What you need to do is to have decent knowledge about ML theory and good dev skills. You don't need to be able to answer the most obtuse theoretical questions, but it is absolutely necessary to have a couple of years of previous experience with neural nets, to have trained many models with your own hands, seen the results, made decisions, rinse and repeat.

There is a high chance of getting hired if you have practical skills. Most candidates are just trying their luck, coming to the interview with abysmal knowledge and experience, even when they have PhD's.