Hey everyone

After clearing the phone screen round, I got a call regarding the Applied Scientist virtual onsite round at Amazon.

It will probably be a 5 hour onsite (details are yet to be discussed with the recruiter). This sub has extensive information about the leetcode style questions but I wanted to ask the MLEs, Applied Scientist and Data Scientists on this sub as to what to expect in the ML depth and breadth round and Business application round. And how to prepare for each of these rounds? If you could share your some resources that would be helpful.

Also below are my leetcode stats, from here on I will focus mainly on Amazon but any other suggestions are appreciated.

I have a 60 min phone interview with team lead for Data Scientist position Gen AI at Amazon. The recruiter told me its gonna a soft ball leadership principles. To my surprise its not leetcosde or any coding challenge at all. They are gonna ask me technical stuff Ig. What I don't understand is how do I say technical stuff without compromising my company's Data. After all I work with Data and issues I get it very much associated with it or even validating it when it has to fly through different Data sources ( like validating through pydantic for llm ingestions ect..) my recruiter advised me to lay out everything in high-level but how do I do it? I have no idea what kinds of questions I am gonna face. So if anyone from Amazon or have idea on this please help me out.

Many asked so I wanted to update, that was the most awful interview experience I ever had in my life. The interviewer seemed absolutely uninterested from the beginning doing his own work he did not care for my name either just straight up started shooting questions but technically I answered everything although he was annoying and did not let me speak for at least a minute. Like bro, he kept cutting me off and did not care for what I have to say but if he got the exact word of what he wanted to listen he’d shift for another question. The recruiter in the initial round had told me I’d not have any Leadership principles so I didn’t wanted waste time preparing for that as it was a screening and I had to clear it for 6 round loop, but the last 20 mins he grilled me on LP questions which I honestly fumbled a bit. On and on it was most terrible rude interview I have ever given my suggestion to expect the worse and prepare for that hopefully these kinda people are rare as many Amazon employees told a lot of interviewers wants you to have the job so they are really patient and listen to what you have to say. I wish anyone who is reading this a very best of luck and I hope you get the job.

I recently went through the Amazon interview process for an Applied Scientist position in Berlin.

Before talking about the process, here is a quick summary of my background. I am a recent PhD graduate with a good track record of publication and one patent. Since my program was an industrial Ph.D., I also worked as a data scientist for an automotive company in Germany. I went through Leetcode up to level medium to prepare for this role.

I talked to the recruiter first, and she told me that the position is for L4 or L5, depending on my interview performance.

Online screening (Code + Deep dive in my CV, 60 minutes): The code was easy-level Leetcode with a little twist on its application to Machine learning. The interview took 15 minutes. Then, we just talked about my past experience. It's worth noting that the area of position (recommendation system) was not very relevant to my past experience. Nevertheless, the interviewer was happy, and I went to the next stage.

The loop:
It was online, consisted of 5 interviews, and spanned across two days.

• Code challenge + 2 LP questions: The code was again easy-level string manipulation. It took about 15 minutes. The rest of the interview was LP questions and a detailed explanation of the position.

• Breadth of Knowledge + 2 LP question: It was mostly about neural networks, like overfitting, underfitting, etc. Most of it was simple, aligning with the topic provided by the recruiter beforehand. However, at the end, the interview went towards detailed questions on transformers, which were not part of the provided topics. I would say I was lucky to get through the last part since I read about Transformers in detail for another interview recently.

• Depth of knowledge + 2 LP question: This one caught me off guard. The recruiter told me that this part would be a deep dive into a topic relevant to my past experiences. Still, the topic was not relevant to my past experiences and heavily focused on mathematics around neural architecture. How loss is defined and how various tricks affect the model performance. Again, I got kinda lucky, since once I had to prepare a presentation on this topic for a seminar. Still, I fell short on one of the questions.

• ML system design + 4 LP questions: It starts with very general questions like designing a recommender system for a given scenario, and then through asking questions, the scenario becomes clearer. I guess it is about asking the right questions and being able to justify the choice. The part for LP questions was longer, so we just talked about the system for 15 minutes. As I mentioned, my background is not in recommendation systems. I spent 2 days reading about problems in this domain and an overview of possible solutions, and I would say it was enough to hold a conversation and give relevant answers.

• Bar Raiser (4 LP questions): Here is an explanation and suggestion for all LP parts, including this one. There are abundant questions available online on Amazon's website and elsewhere as well, and all of the questions were from these resources. There were no surprises. You need to get prepared for them with 10 stories and be prepared for follow-ups as well. Also, be ready for failure cases. To be efficient regarding stories, I prepared two versions of them, where one succeeded and one failed. So you can double your stores while many details can overlap.

Outcome:

The recruiter scheduled a call on the fifth day. She told me that, based on the feedback she got, there was no negative feedback regarding technical or behavioral competencies. However, they are not going to give me an offer since the team feels that I do not have enough experience and suggests that I "go work somewhere for 10 months or so and return again." So, it was a reject.

Honestly, I do not know how to interpret that. The experience is written in my CV, and we went into all the details in the online screening session. A friend of mine who is working at Amazon told me that they most probably made an offer to someone for the role before, so the role was filled before my process concluded. I think his speculation is correct since the job posting was removed from the Amazon website a few days before my loop. All in all, it was a stressful 45-day process that ended in disappointment. I’m now trying to shake off the setback and continue my job search. Wish me luck!

This is a follow-up to my earlier post (LINK). I recently went through 7 interview rounds—2 phone screens and 5 onsite rounds—for an Applied Scientist 2 position.

The phone screens focused on machine learning (ML) fundamentals, statistics, probability, and a few basic data structures and algorithms (DSA) questions (though I don't recall the exact ones). The 5 onsite rounds were as follows:

1. ML Breadth Round: Covered a wide range of ML topics with a heavy emphasis on math.

2. ML Depth Round: A deep dive into the specifics of my resume and past projects.

3. Business Problem Round: I was asked to design Alexa from scratch—not the software system design, but the ML system design. This included identifying necessary datasets, tasks to be performed, model selection and justification, and evaluation metrics.

4. Behavioral Round (1.5 hours): A rigorous behavioral interview focused on leadership principles.

5. DSA Round: Two questions were asked—one similar to the course schedule problem, which required topological sorting, and the other was about finding the longest duplicate substring in a given string.

Although I wasn't offered the L5 (Applied Scientist 2) role due to my relatively limited industry experience, I did receive an L4 (Applied Scientist 1) offer, and it was at the top end of the L4 salary band. My next goal is to work hard and earn that L5 promotion next year.

For context, here's a snapshot of my LeetCode journey so far:

A few days ago, I posted about passing my first-round Amazon DE interview, and a lot of people asked how I prepped. Since I’m still in the middle of interviews, I figured I’d share what worked for me, what didn’t, and some things I wish I’d done differently.

1. What Actually Mattered in My Prep

After reading a ton of interview experiences and talking to current engineers, I realized I needed to focus on:

• SQL & Python – Easily the most important part. I practiced writing efficient queries, handling edge cases, and explaining my logic clearly.

• Data Modeling & System Design – Even as a new grad, they expected me to understand table design, indexing, and trade-offs. I focused on star vs. snowflake schemas, normalization, and partitioning.

• LeetCode (But Not Too Much) – Stuck to mediums. I was tempted to grind hards, but it felt like overkill.

• ETL & Pipelines – I made sure I could explain batch vs. streaming, basic Airflow concepts, and how data moves in a system.

• Behavioral Questions – I thought this part would be easy, but I kept rambling. Eventually, I wrote out key stories and practiced saying them out loud until they felt natural.

2. My Experience Using AI Interview Tools

I didn’t plan on using AI tools at first, but after some rough solo practice sessions where I couldn’t get through a coherent system design answer, I figured I’d give one a shot.

Here’s where it actually helped:

• Made My Answers More Concise – Turns out, I was over-explaining everything. Getting real-time feedback forced me to be more direct.

• Structured My Prep – Instead of jumping between random SQL and system design questions, I followed a focused session that actually felt like an interview.

• Saved Me Time – No more guessing if my answers were solid. I got immediate feedback on where I needed to improve.

Would I replace real mock interviews with an AI tool? No. But as a way to refine answers and avoid second-guessing myself, it was surprisingly useful.

3. What I’d Do Differently Next Time

• Start Prepping Behavioral Answers Sooner – I underestimated how much these matter.

• Do More Mock Interviews – The first few times I had to explain my system design answers out loud, I completely fumbled.

• Use AI Tools Earlier – I only started using them at the last minute, and they actually helped more than I expected.

For anyone else prepping for Amazon DE interviews, how are you approaching it? And if you’ve used AI tools, did they actually help or just add more noise?

Hello everyone,

I want to provide my experience with Amazon Applied Scientist interview. I took a lot from this subreddit and similar communities and want to give back. I hope this will help some folks, especially those with academic background. I got an offer for L4 (Applied Scientist I) at the end of the process.

My background is that I obtained PhD in a non-ML field a year prior and then worked for a e-commerce company as an ML scientist before getting laid off. I have therefore ~4 years of academic experience and ~7 month of industry experience.

I start with the interview structure first, and then share how I prepared for technical and behavioural part. I will not share exact questions for obvious reasons, but everything was very similar to what you find online (on reddit or especially glassdoor).

Part one: interview

Phone screen (1hr):

• quick talk about a favourite ML paper (10-15 mins).

• ML coding question: implement an optimisation algorithm from scratch in Python (~20 mins).

• 3 LP (Leadership principles) questions, to one of which I did not answer.

Here I make a little note that I justified that I don't have a good story this one question. I read somewhere that it's better to not give an answer rather than give some trivial (or 'Bar-lowering') example. However, Later in the onsite prep-call with the recruiter I asked if its is OK to NOT give an answer, and she told that its better to at least say something. So it's still not clear for me what would the best tactics be. Don't put 100% trust into internet advice (including this post!).

Got positive phone-screen outcome email three hours after the end of the interview.

Prep call with a recruited (45 min):

Definitely very useful, take it if you can. It will give you a broader overview of topics in each part. You can find applied science topics on the internet, but prep call gives you a bit more information and expectations.

Virtual onsite (five 1h interviews, 15-60min breaks in between):

all loop interviews were more than 50% behavioural (LP questions) - keep this in mind. I'm talking about first 30-40 mins of each interview be about LP.

1st round (ML breadth):

• 5 LP questions.

• ML breadth questions about linear regression, KNN, types of supervision and so on.

Note after the first round: usually it is expected that each interviewer will ask 1-2 LP questions to test some principles. Here got 5 and it was obvious that they did not collect evidence from stories I told. It worried and demoralised me very much and I thought I failed this round. On top of that some of my ML answers were not complete... Lesson I learned here is to not be discouraged if one interview (even the first one) goes not ideally. I performed much better on the later loop interviews.

2st round (Bar Raiser):

• 3 LP questions

The bar raiser was very positive and supportive, which helped me to overcome discouragement after the first round. LP question were discussed very deeply, with follow-ups on both behavioural part (e.g. impact) and technical part (how I interpret why model performed better compared to baseline). Very pleasant round and I think I nailed it.

An example of a non-trivial BQ (you can find it even online): time when I delivered something for customer that liked, but they did not knew they needed it.

3rd round (Coding):

• 3 LP questions

• Programming question

This was the hiring manger interview. Coding question was not leetcode-style, it was a string manipulation question which is solved with one for loop and a couple of if-else statements. Here one, as usual, thinks out loud and consider assumptions and edge cases. Eventually I was asked to implement the solution for the exact question I was given and do not try to make it more extendable or generally applicable. Here I got a bit confused by the logic and code was not super-readable, but we did not have time to adjust it.

Additional 15 minutes (on top of 1h interview) HM explained the role and answered my questions. Good round, but my programming could have been better.

4th round (ML breadth?):

• 2 LP questions

• ML topics

Here I expected to be the ML-depth interview (when I am asked about my projects), but the LP questions smoothly transitioned into ML breadth discussion. I was asked about NLP and then about tree-based ensemble methods. Since I worked with ensemble methods before, we did a deeper dive into how training it performed, what are the industry standards and so on. Round went really good.

5th round (Science application round / miniature system design):

• 4 LP questions.

• ML research problem related to the role

On the last LP question, I had to repeat the story I gave during the bar-raiser. But obviously I tried to adjust the story towards the particular question which was different from the bar-raiser question. Surely during the debrief they should have noticed that, but I could not come up with another example.

Science application part is to design a system relevant to the role, but with more general discussion (e.g. start with number of users, ask if there is a system in place which already produces output and log data, if not, how to build data-collection system and so on, batch vs real-time processing, A/B test). Definitely here I made some mistakes like not asking some important clarification questions but overall I did a good job. Without preparation, I would not have passes this technical question. Formally this is NOT ML system design, but just a science case study.

Phew... that was very intense and draining - be ready for that. You may opt to split the loop in two days.

On the fourth day after the loop I got an email with subject 'amazon outcome' and was invited to schedule a call. We scheduled it next day and I got a verbal offer, asked for starting date and salary expectations. Waiting for the outcome is mentally very tough, be prepared for that.

Part two: some preparation tips

Coding:

By the time of the onsite, I had around 120 leetcode problems solved. In the last weeks I focused on the Amazon-tagged problems of easy and medium difficulty with arrays, strings, two-pointers and other not-so-advanced algorithms. Honestly coding task I was given on the onsite is not leetcode-style at all.

ML breadth:

Skim the list of topics recruiter will sent you. You are not expected to know everything, it's OK to not know about some niche subjects. But I believe that knowing about popular themes (e.g. Transformers) is essential even if you go to Fraud detection team.

ML systems:

Due to the lack of time I studied ML design only for systems relevant to the role. Recruiter told beforehand that design task is very likely to be about the team's job. This task is about thinking about customer experience.

ML depth:

You need to be ready to go into detail of your work. So if you published a paper three years ago and don't remember much, better to re-read it and think about decisions you had to make to chose one approach over another.

Leadership Principles:

Here I will elaborate, since a lot of people asked in DM about how I prepare these. It will be relevant for all roles of L4-5 levels. For me, the largest obstacle is mapping Amazon's principles to stories from my PhD. Due to the limited experience in industry, out of my ~20 stories only 5 are from industry (+story from my industry hackathon experience).

Most important prep tip for LP: story bank.

I prepared my story bank with the help of AI. Create stories using STAR format, paste it to ChatGPT and ask to format it towards Amazon LP in a more concise way. Prompt it with the role and level you are interviewing for. Don't forget to include metrics of success whenever possible. Make as much non-trivial stories as possible. Obviously check ChatGPT answers, as it tends to replace/omit details. After you have created stories (I made a bit more than 20 stories), save them In a pdf, feed this pdf to ChatGPT and ask to create a table with a list of stories and LP it covers (usually story covers 2-3 LPs). Find which LPs are strongly present and which are week/absent. Note that you will not be asked fours LP out of 16 total. Then iterate: either add stories or adjust some stories to fit more LPs. Hardest part for me were stories about tight deadlines, conflicts and customer impact.

Don't overrely on ChatGPT: I mostly tried to map my academic language into something an Amazonian would like to hear, and emphasise impact.

For academics: customer obsession works in science too! For example, your customers are your fellow researchers which will use your papers in future. How to do you think about those people when writing a paper? May be you open-source your datasets and code for the ease of reproduction? Or may be you help your co-author with refining selection criteria to reduce false positive in the paper's catalogue? All those are examples of several LPs.

On using notes: you can and should use notes during the LP questions. I prepared my list of stories as collapsable sections in Notion and just unfold it once I see the story fits the question. You may take a few seconds to skim the story and notice key points (highlighted in bold). Once you start talking, you may reference your notes but obviously do not read from the screen (you will loose fluency and it will not sound natural). Couple of times I told interviewers that I want to have a minute to think about the question and select a story from my list. It was completely OK.

Good luck!

Hey everyone, I’m looking for advice as I’ve cleared the phone screen and now have a 5-round Amazon GenAI Data Scientist interview scheduled next month: 1. ML Breadth 2. ML Depth 3. Python + SQL 4. GenAI Applications 5. Leadership Principles

What kind of questions and problems can I expect in each round—especially GenAI and ML depth? Will I need to build ML algorithms from scratch, focus on pandas/SQL, or design GenAI applications? If you’ve interviewed for a GenAI/Data Scientist role at Amazon, your insights would be hugely appreciated!

Thanks folks!

Hello Peeps,

I have interview at Amazon for Applied Scientist role based in SD. Can someone send me list of Leetcode questions I should look into. I have heard basically they'll give question similar to SDE-1 level, I'm not sure. If someone can share the updated list that would be really helpful.

I have a technical phone interview with Amazon for a data science role coming up. The interview will involve ML breadth, leadership questions, and Python coding. All of it in 1 hr.
My question for the coding round is - Do you think the interviewer will likely ask questions related to pandas and numpy, or data structures(DS) type of questions? I would prefer they ask numpy/pandas, as I know it well. But want to be prepared for DS type, if yes, what type of questions/topics should I solve and what difficulty?

While gathering information from the recruiter, this is what I gathered - 

problem-solving skills 

data manipulation

extract, prep and get the data ready

manipulate in a certain way

if I can recognize a pattern.

Logical approach. Pros and cons of solving it using one method vs other

wont be able to run or execute.

More of numpy and pandas type of things. Little bit of algorithmic. 

I got the schedule for the loop 5 days after the phone screen.
The email mentioned that there will be 6 rounds. The first one is a technical presentation. The other 5 are science breadth, depth, ML applications round, coding round, and bar raiser round.

I have done lots of research presentations before, but I’m just curious about their expectations for the technical presentation. I’d like to know if I should present multiple connected projects under the same theme of research, or if I should pick and present a project in more depth.

For ML breadth and depth, do they tend to ask ML questions related to the team’s work, or can it basically be anything in ML?

How much LeetCode do I need to prepare for this round? Right now, I have around 100 questions solved. Or are they going to ask ML-related coding in this round?

Is there any resource I can use to prepare for the ML applications round, or any tips for that? I haven’t done this kind of interview before. I heard a book called ML System Design is good for this.

Any advice or tips are appreciated. Thank you!

Alright, Let’s use this thread to post the interview results/experience of Amazon SDE1.

Please use this format:

<Location>,<Interview Date>,<Result>,<Response Time>

<Interview Experience>

Example can be found in the first comment.

Other than Kaggle

Until now, there was an online assessment that I aced. In the first interview, I faced medium to hard LeetCode coding questions, completed all of them, and the interview couldn't have gone better. Today marked the final interview, during which the interviewer asked extremely advanced and out-of-syllabus questions for the ML role. Being in my fourth year of university, where intern questions are typically expected to be basic. However, the questions posed were very difficult, and I stumbled on almost all of them. The first interviewer concluded the session midway, asking if I had any questions, suggesting that we should wrap up.

Fortunately, the second interviewer joined the conversation, offering me another chance. This time, the focus shifted to my previous internship experience and the technologies I had used. I seized this opportunity to explain everything in good detail, as it aligned with my strong familiarity. This served as my final attempt to showcase my knowledge and experience, and to my relief, I answered all the questions by the second interviewer perfectly.

Despite this, I don't have a good feeling about the outcome, and I am genuinely disappointed in myself. Despite everything going so smoothly in the previous stages, I feel that I faltered in the final round.

Hello everyone,

My first post in the sub-reddit.

I recently interviewed for a L6 position but was offered a L5 role.

Interview exp:

1. Research talk and in-depth theory exploration

2. Leetcode (easy-medium)

3. System design: Deploying ML solutions from scratch for a given task (be sure to bring in LLMs somehow;))

4. Fundamentals of ML: Classical ML algorithms from scratch

Edit: removed TC because it seems better to discuss this on blind or elsewhere.

I have a data engineer call with amazon next week, and I'm worried as I dont have a skill set in dsa. I know basic data structures in python. Do amazon expect data engineers to know dsa? Also how crucial is time and space complexity questions for data engineers? I'm literally nervous about how some people were rejected even when they answered all the questions. Any tips on the kind of python questions that would be asked for amazon data engineers roles, concepts I need to be aware of would be so much appreciated. I'm kind a nervous about this. Thanks

Hey everyone! I have a technical phone screen interview with Amazon coming up. Has anyone else gone through this? I’d love to hear what to expect and any tips on how to prepare!