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Amazon Applied Scientist interview round 1: 1-hour machine learning fundamentals and coding.
Read how ML applied in industry. Learn how to pass ML interview and ML design interview.
I personally collected list of questions for Amazon Applied Scientist and Data scientist positions. This list excludes Leetcode, domain specific questions, i.e, Automatic Speech Recognition, NLP and Computer vision.
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Do you think more people in tech would benefit from having a humanities background? You studied Psychology as an undergrad. What are some ways in which that has helped you in Data Science?
Having a humanities background is associated with certain traits: Being more open-minded, critical thinking, better problem framing, research skills, and the ability to communicate with laymen. I think such traits would benefit everyone, not just tech folks. While a humanities degree helps with cultivating these traits, there are plenty of other ways—it can also come from having the opportunity to work on diverse, challenging problems, good role models, and work experience.
Other than the traits mentioned above, my Psychology degree taught me how to analyse qualitative and quantitative data. It also taught me about statistics (and how to be skeptical of it). In addition, I learned about how people perceive, think, and behave; this helps when I’m building customer-facing machine learning features.
What’s your least favourite part about being an Applied Scientist?
I’m still learning about how to manage this, but sometimes, I spend more time than I would like writing documents and in meetings. Nonetheless, it’s essential for socialising ideas and getting buy-in and feedback. I just wish I was more effective and faster at it.
Occasionally, stakeholders suggest solutions that are far more complex than it needs to be. I blame the overhyping of technology and machine learning in the media. When this happens, our team patiently tries to understand their perspective and educate them. Nonetheless, it takes considerable time and effort and distracts us from work that helps customers.
Lastly, because my work revolves around data, I’m also constrained by access to high-quality data. Delays happen now and then. Sometimes, it’s a minor lack of permissions which takes a few hours to a few days to resolve. Other times, we find that our system isn’t tracking a specific field and we need to update our trackers and wait a few months, or backfill the data.
My friend is a PhD with 4+ years of experience at non-FAANG company. He recently joined Amazon as Applied Scientist. He shared how he prepared for the onsite interviews. Machine Learning Design Interview book on
Below is his experience with Facebook and Google (repost from leetcode).
I finished 350 questions on LC over 9 months. (overall LC count — 633–100 hard + 533 (medium + simple)). I did very exhaustive ML prep and finished the Grokking System Design course for System design for FB. Overall, I prepared very intensely for 3 months maxing out most of my day including squeezing time at work to prepare. I am disappointed with the FB outcome given my prep and recruiter response, but there are much more worse things happening in the world now. Wish everyone chasing FAANG and other top companies all the best!
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Recruiter sent a one line reject email within 3 days with no feedback whatsoever.