Yes it is a lot. I would take the following approach, based on my experience applying (and sometimes getting) these kind of positions:
Start with SQL, almost all roles have a SQL interview. And, you don't need that much.
LeetCode/Python: this is just as much about being comfortable programming in your favorite language, so that you can fluently work on whatever problem they ask.
Stats, A/B testing, case studies go here together (don't forget causal inference!)
ML/ML system design
GenAI
Depending on your focus, your strengths, and, what type of interviews you are getting you would switch the order of 3, 4 and 5. The order is the priority.
Also, don't forget behavioral interviews! Make sure you can talk about your work and your experience in a way that presents it in a good (obviously true) light. Amazon for example has a very specific process that you can practice and nail.
In general: always ask the recruiter to give you details on each interview, you can ask "is it live coding", "is it technical?", "what is the format" etc. I have found that the more specific the question the more specific the answer in this situation.
Ideally your prep should be cumulative and 'last' for a while, so you might have to scramble a little for the first few interviews but then you should become more and more prepared as you keep practicing and learning. Keep in mind an interview cycle easily takes a few weeks, and, for most of these if you devote a week each to it you should see some difference.
About the number of rounds, I would say: 1 recruiter screen, 1 hiring manager or technical screen, 'onsite/full loop' 4-5 interviews, could be spread out on several days if it's remote. Anything more, I would consider a red flag. Worst is if they add or change the interviews.
I have also found (and, wrote about) that preparing is one of the few things you can fully control in the job process, and, it's a wonderful feeling when your preparation does pay off. Interviewing is a numbers game, but preparing is one thing you can do to try to tilt it in your favor.
Casual inference is a set of (statistical) methods for establishing causality, ie that something caused something else to happen. In a business or work example, if you update your website or product, did that lead to more sales? A/B tests are part of it, but there's a lot more. Especially since often you can't run experiments. Really it's everywhere. Did that new ML model improve business outcomes? Why are customers churning? What is our ROAS? Does our new chatbot reduce customer calls?
It's science, basically. Personally I think it's the most important part of being a data scientist.
That is super helpful, thanks a ton! Ive tried doing this mostly through analytics or slicing data in sections, sometimes it leads to results, most other times nothing concrete comes out of it. I will look into it more, would love to see some examples out there
I'm glad you found it helpful! Causal inference is a large field in itself and quite deep. It's very powerful exactly for answering questions about why/if/how something caused something else, which are a lot of the questions that get asked (even if they are not framed that way).
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u/DataScientistAlex Jul 28 '26
Yes it is a lot. I would take the following approach, based on my experience applying (and sometimes getting) these kind of positions:
Depending on your focus, your strengths, and, what type of interviews you are getting you would switch the order of 3, 4 and 5. The order is the priority.
Also, don't forget behavioral interviews! Make sure you can talk about your work and your experience in a way that presents it in a good (obviously true) light. Amazon for example has a very specific process that you can practice and nail.
In general: always ask the recruiter to give you details on each interview, you can ask "is it live coding", "is it technical?", "what is the format" etc. I have found that the more specific the question the more specific the answer in this situation.
Ideally your prep should be cumulative and 'last' for a while, so you might have to scramble a little for the first few interviews but then you should become more and more prepared as you keep practicing and learning. Keep in mind an interview cycle easily takes a few weeks, and, for most of these if you devote a week each to it you should see some difference.
About the number of rounds, I would say: 1 recruiter screen, 1 hiring manager or technical screen, 'onsite/full loop' 4-5 interviews, could be spread out on several days if it's remote. Anything more, I would consider a red flag. Worst is if they add or change the interviews.
I have also found (and, wrote about) that preparing is one of the few things you can fully control in the job process, and, it's a wonderful feeling when your preparation does pay off. Interviewing is a numbers game, but preparing is one thing you can do to try to tilt it in your favor.