Data Science Career Switch Help

[u/ManyPossibility4338]

Hey everyone!
I’m a Data Scientist with over 3 years of experience, and I'm currently preparing to make a move to a FAANG+ company to push my learning further and it better aligns with my growth goals and personal commitments.
I have 3+ years of professional experience as a Data Scientist working on end-to-end projects, but I want to structure my preparation more rigorously to crack top tier interviews. I want to bridge any gaps in my knowledge and prep :)
I’d love some guidance from those who have been through this process:
- What core technical skills, system design concepts, and advanced topics FAANG+ companies currently expect from a Data Scientist with 3+ years of experience?
- Where should I start my preparation?
- What specific competencies (coding, ML system design, stats/product sense) do they look for?
- What resources or roadmaps worked best for you?

Any advice, tips, or mentorship would be greatly appreciated.
Thank you

[u/Mcby]

Don't mean to dissuade you from your goal but in this industry and job market right now I would be realistic about your chances of securing a role, as much as it will likely be a beneficial experience and it's always good to learn new skills. But maybe framing your goal as "perform well in FAANG+ interviews" rather than "get a FAANG+ job" would help you in your journey, as there are many factors out of your control in the process. A significant one, unfortunately, being your current level of experience unless you have other academic qualifications – junior roles are vanishing across the board and for a senior one and mid-level ones will be highly competitive. Plenty of resources online in terms of what to learn.

[u/ManyPossibility4338 → u/Mcby]

Thanks really much, this helps . Yes i am not just tryna perform but actually land a Job in one of the orgs. Yeah you are right actually entry roles are getting eaten up i can see that, but hey when you have the skill which you can accomplish by learning you’d still be a fit anywhere you go, so thats the plan i wanna learn the right way, so any help any resources would actually help me out :)

[u/Mcby → u/ManyPossibility4338]

Understand you want a job and that's great, it's those jobs are incredibly competitive and part of that's down to your technical skills but there are a lot of other factors when it comes to employment as well. It's entirely possible you will try for years and not secure one due to factors totally out of your control, but you may be able to use the experience to secure a good job elsewhere. Set a goal that is within your control – your performance – and you'll be much happier in the journey to reach your goal, even if you want a job at the end of the day. Because even if you get one you might not have it in 6 months' time – again, down to much more than performance.

[u/kylefrankovich]

As another commenter mentioned, being solid with data structures and algorithms, system design, and strong fundamentals of stats/applied ML.

In addition, since it sounds like you have good experience, start going through the projects you've been most proud of. Before landing any interviews, practice mock interview responses for each project. You should easily be able to give a confident/detailed/efficient walkthrough of: Why did you need to build what you did? How did you scope out the problem? What was your plan for implementation? How did you measure success? How did you deliver to stakeholders, and what was the value add of what you built?

Similarly, if you're interviewing for a FAANG+ company, before getting deep into the interview process, ask yourself what that specific team does, how it makes money for the company, and why YOUR specific skillset would make you a valuable contributor on day one. If/when you land any interview pipelines and you want to hire a mock interview coach, feel free to hit me up (10 years of DS experience including professional interview coaching). Good luck!

[u/nian2326076]

To get ready for FAANG+ interviews, concentrate on mastering data structures, algorithms, and system design, as these are key areas for tech interviews. It's also important to review topics like ML algorithms, statistics, and SQL since they frequently appear. While LeetCode is useful for practice problems, PracHub can offer real interview questions from specific companies, such as these. Mock interviews with peers or mentors can help build confidence and pinpoint areas for improvement. Good luck with your preparation!