Startups are all the rage these days, and many people are attracted by some of the benefits: lots of toys in the office, possibility of remote work, flexibility and an informal office culture. This is all fine and well but it comes to a cost. Does that mean you should go to a startup in your first job as data scientist?
Startups, by definition, do not have a tested business model. Maybe there is a need in the market, but maybe customers are not ready to pay. There will be likely a lot of iteration in the product, and that very often translates into changes in the internal processes.
This can be terrible news for someone who is starting in their career as a data scientist:
Tasks might not be clearly defined
There will be constant changes in the data models
Objectives and likely very little consistency in the day to day work.
Why is this bad for you as a beginner? Constant changes distract you from getting proficient with a single set of tools.
Ok, so should you avoid startups?
Don’t get me wrong, I think that data scientists should be proficient in many tools. After all, the work is largely the same, whether you use R, Python or a no/low-code solution like Alteryx. But the constant changes in the beginning distract you from grasping the fundamentals of real-life data science work.
Startups are fantastic places to go as your second job, once you earned enough experience in a corporate environment or a larger company. You will really appreciate the flexibility in the work, and since you will be, at that point, much more familiar with your tools, you will thrive in the wonderful chaos a startup can be. But I would highly recommend beginning your career in a more structured environment. That does not mean you cannot succeed in a startup in your first job as data scientist, but you should be aware of the risk.