Hi all, I wanted to make a post about my experience with finally getting into FAANG!!! I am sure some of you have seen my posts on here lately where I shared my experience followed by a rejection from various FANG companies. Anyways, really happy I got an offer at Amazon and will now proceed to share my experience.
About the job:
The position did require a maters degree in some science discipline with a PhD preferred preference. The jobs responsibilites is 90% machine learning where you will be working along side research scientists, applied scientists, and data engineers.
Background:
I have a undergrad from top tier instutition in both Mathematics and Business. I also have a masters in Mathematics from top tier institution (PhD dropout). I currently have about 2 years experience working as a Data Scientist at a private equity company.
Process:
I was reached by a recruiter on LinkedIn to see if I was interested in interviewing with Amazon for a Data Scientist role. The first phone screen was just with the recruiter to explain the position and gauge my interests. I then had a second phone interview with a Research Scientist at Amazon. That interview was half behavioral and half technical. The techincal part was to explain in depth a machine learning project I did at my work, why I used that particular model, what I did to evaulate it, etc.
The call went really well, and I was told I will now be moving onto the onsite. For the onsite portion it consisted of 4 interviews. All of which would have a behavioral component as well as technical.
The behavioral component was similar to all those questions someone was kind of enough to post on here. The techincal portion consisted of mostly machine learning questions. For example, take me through a project from start ot finish, why did you choose that model, how did you evaluate it. Also, some of the questions were situational, for example, we are trying to develop a spam classifier, how would you solve the problem, what are the pros and cons of this approach. There was one coding round but was a leetcode medium or easy question, I do not rememeber the question but I am pretty sure it was in the top 100 Amazon questions.
Advice/Material Used:
I went through Andrew Ng course introduction to machine learning and I purchased Chris Albon's machine learning flashcards. Both of these were more than enough to prepare for the interview. I would say though practicing speaking out loud is really important. A year or so ago I found sometimes speaking about machine learning/deep learning convoluted to describe, so it is good to practice so that when you have an interview you sound really solid.
In reference to the latter above, I came from a PhD program in mathematics and althogh I did not finish I did pass both qualification exams. The material you would need a sufficient mastery of includes but is not limited to statistical inference, probability theory, linear algebra, and numerical mathematics. If you want to be able to do research then you would need to take graduate level courses in numerical mathematics as well as real analysis and measure theory each for at least 1-2 years.
Results:
I got an email a few hours after from my recruiter that I have passed the onsite will now move onto the offer stage. I am currently in the team matching phase right now, so I will update this post once I got all the details and stuff sorted out. I am not going to appear as anoynmous since I signed an NDA to not go into depth about the process.
Done with team matching, accepted offer!
Links:
I am not promoting or encouraging anyone to purchase these flashcards. They are what I used to prepare and I also rewrote most of them so that they were in a better format.