Hi there,

next week I'll have my first interview for the position. It's a phone interview with a Senior Applied Scientist.

I've heard that especially Amazon is very particular about their behavioral questions. How can I prepare for it? Do I have to follow strictly their principles like "customer obsession" etc. a? Are there any good ressources for it?

It's my first interview for that position. Should I expect mostly:

• a casual walk through my CV and recent projects?

• coding/leetcode styled questions or hands on coding (data cleaning, modeling etc.)?

I really don't know what to expect/what to focus on. Would you share your experiences? I would assume that a Senior Applied Scientist would not care too much about the behavioral stuff and focus more on the technical details, but I could be totally wrong.

Edit: It was purely technical.

I am currently a Lead Data Scientist at a large defense contractor, primarily applying data science solutions to business-facing homerooms. Think supply chain, business management, etc.

A few highlights about me...

• Very strong SQL skills, and I have done a large amount of data ETL

• Moderately strong Python skills

• Top 1% on Stack Overflow (I answer a lot of SQL and Python questions, also ask some)

• Nearly 10 internal Trade Secrets awarded to products I have built

• B.S. in Information Technology, I am graduating in August with my M.S. in Computer Science w/ an AI concentration from Hopkins

• About 3.5 years of work experience out of undergrad, two internships at Defense contractors before that

• Also have security related certifications (Security+)

• I mentor both the cybersecurity and AI clubs for my high school (along with a few other alumni)

I was contacted on LinkedIn by a recruiter. I have never really had an intention of working at FAANG organizations. From what I have read both on Reddit and elsewhere, the "work 7 days a week" and high pressure culture doesn't fit what I am really looking for. However, the recruiter mentioned almost 60% more than I make now, so that was enticing.

I feel technically sound -- but I definitely don't know how succinctly I could give an answer to some technical questions. I've looked at:

https://towardsdatascience.com/the-amazon-data-scientist-interview-93ba7195e4c9

https://towardsdatascience.com/amazon-data-scientist-interview-practice-problems-15b9b86e86c6

https://www.reddit.com/r/datascience/comments/dn5uxq/amazon_data_scienceml_interview_questions/

Are these good resources? Should I be prepared to write an algorithm from scratch? Would it be easier things, like kmeans, or am I expected to code backprop from scratch? I've done these things from scratch before, but I used reference material... I am nervous about not being able to demonstrate my skills because of being too focused on providing these overly technical answers.

Any advice is appreciated!

Edit: Wow! This blew up. I certainly was not expecting this much feedback, and certainly not so much kindness. As a somewhat new graduate ( < 5 years) who is still figuring out their own self confidence, getting to share a little bit of my background and my fears moving forward with you all has been cathartic, not to mention the sheer volume of incredibly useful feedback I have gotten. I am going to think some thing through tomorrow, and I'll be sure to update this post. If I go along with the interview, which I think i will based on this feedback, ill be sure to create an update post to let you all know what happened!

I have my final round of interviews coming up for a Data Scientist position at Amazon, and I'm looking for guidance from anyone who has experience with their interview process or similar roles. Here’s what I know so far:

• There are 5 interviews scheduled: Data Science Breath, Data Science Depth, Data Science Application, Coding and Behavioral.

I’d love your input on the following:

1. Interview structure: What can I expect in terms of topics or focus areas for each?

2. Technical prep: What kind of questions or challenges should I be ready for? Any specific areas of data science (e.g., machine learning, coding, statistics) that Amazon tends to emphasize?

3. Behavioral round tips: What qualities or experiences does Amazon value in candidates, and how can I best showcase those?

4. Resources: Are there any prep materials, mock interview platforms, or study guides you’ve found particularly useful for Amazon interviews or similar roles?

I’m eager to give this my best shot, so any advice, anecdotes, or pointers would be incredibly helpful. Thanks in advance!

Hi, I was recently contacted by an Amazon recruiter. I will be interviewing for an Applied Scientist position. I am currently a DS with 5 years of experience. The problem is that the i terview process involves 1 phone screen and 1 onsite round which will have leetcode style coding. I am pretty bad at DSA. Can anyone please suggest me how to prepare for this part in a short duration? What questions to do and how to target? Any advice will be appreciated. TIA

Hello everyone

I have a phone screen round scheduled at Amazon for the position of data scientist. The recruiter told me it will be based on ML breadth and depth. Some glassdoor reviews say it can be a full coding round. I'm kind of confused how to go about it. I know process can vary but still will appreciate inputs from those who have gone through this process. What all should I expect? Please let me know.

I've been trying to learn some fundamentals of data science and machine learning recently when I ran into this medium article about Amazon interview questions. I think I can answer some of the ML and probability questions but others just fly off the top of my head. What do you all think ?

• How does a logistic regression model know what the coefficients are?

• Difference between convex and non-convex cost function; what does it mean when a cost function is non-convex?

• Is random weight assignment better than assigning same weights to the units in the hidden layer?

• Given a bar plot and imagine you are pouring water from the top, how to qualify how much water can be kept in the bar chart?

• What is Overfitting?

• How would the change of prime membership fee would affect the market?

• Why is gradient checking important?

• Describe Tree, SVM, Random forest and boosting. Talk about their advantage and disadvantages.

• How do you weight 9 marbles three times on a balance scale to select the heaviest one?

• Find the cumulative sum of top 10 most profitable products of the last 6 month for customers in Seattle.

• Describe the criterion for a particular model selection. Why is dimension reduction important?

• What are the assumptions for logistic and linear regression?

• If you can build a perfect (100% accuracy) classification model to predict some customer behaviour, what will be the problem in application?

• The probability that item an item at location A is 0.6 , and 0.8 at location B. What is the probability that item would be found on Amazon website?

• Given a ‘csv’ file with ID and Quantity columns, 50million records and size of data as 2 GBs, write a program in any language of your choice to aggregate the QUANTITY column.

• Implement circular queue using an array.

• When you have a time series data by monthly, it has large data records, how will you find out significant difference between this month and previous months values?

• Compare Lasso and Ridge Regression.

• What’s the difference between MLE and MAP inference?

• Given a function with inputs — an array with N randomly sorted numbers, and an int K, return output in an array with the K largest numbers.

• When users are navigating through the Amazon website, they are performing several actions. What is the best way to model if their next action would be a purchase?

• Estimate the disease probability in one city given the probability is very low national wide. Randomly asked 1000 person in this city, with all negative response(NO disease). What is the probability of disease in this city?

• Describe SVM.

• How does K-means work? What kind of distance metric would you choose? What if different features have different dynamic range?

• What is boosting?

• How many topic modeling techniques do you know of?

• Formulate LSI and LDA techniques.

• What are generative and discriminative algorithms? What are their strengths and weaknesses? Which type of algorithms are usually used and why?”

What's it like working at Amazon as a data scientist? I'm a jr data scientist with 2 yoe looking to transition to FAANG+, but with the economic situation, Amazon is the only company I'm seriously considering. I had a recruiter reach out to me last week and I wanted to hear from those who have worked in data science or machine learning engineer roles.

Is the work life balance as bad as people make it seem and the way the company treats its workers? Which orgs within Amazon are working on the most interesting data science business problems?

Hi Everyone.

Hope you all are doing well.

I am having an Amazon applied scientist interview within a week. This is the first interview, which is a phone screen interview. Can you guys share with me what type of questions may be asked or what questions they focus on in a phone screen interview?

Team: Amazon Music catalogue team ...

it was written like this in the email -- Competencies : ML Depth and ML Breadth

My background:

1. Masters in AI from an top IIT

2. 3 A* publications

3. Research internship at a top research company.

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 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. 

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!

Hi Reddit,

Finally, I've become a grown-up and recently just graduated from my university.

I come from an MSc in business intelligence and have about one year of experience with study jobs, that is mostly focused on "front-end business intelligence". Basically, that means making dashboarding and working with already ready data.

So, now I just hit an interview with Amazon and I'm pretty impressed that they wanna interview with me even though I lack experience, and therefore I'm here to ask for help.

As you can see in the Job Posting I pretty much need two years experience and be proficient in SQL. Well, to anyone's information I'm quite confident in everything than SQL. I know for sure, that this job requires a lot of technical skills within data extraction, manipulation etc for business requirements.

And.. I'm pretty sucky in SQL having only had in school and made some basic query. The interview is on Wednesday, and I don't know if I should just try it? They send an e-mail, that said they will have live coding test on me but I basically am scared because I'm terrible at this part.

What's ur suggestion? I have two days to maybe run a data camp for fundamentals of SQL, but I'm unsure if that's enough to consider myself worthy for this position?

Any comments will be appreciated thanks!

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 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 about the coding question 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.

Hi community, I wanted to share my experience for the 2 roles that I interviewed for at Amazon.

SDE Intern:

Timeline:
applied - Jan 31st
OA - Feb 1st week
VO - March 2nd week
Waitlisted - March 3rd week

Interview experience:
My interview was not like the usual ones. After the introductions, the interviewer set the definition of the interview, saying that they will ask only 1 coding question, and we will go over the approach and solution. So I wasn't asked any LP in this one.

The coding question was about printing node values in a certain order, in a Binary Tree. It took me about 40-45 mins to solve it. I got the initial approach in 5 mins, and started talking about how I would go about it, wrote some pseudocode, and explained why, with a dry run. The Interviewer gave an edge case where this would fail, and I immediately got a better approach in my mind. I explained that and wrote the code quickly, and the interviewer went through code and was satisfied. I asked him questions for the last 10 mins.

My prep:
2 weeks of non-stop leetcode grind (Blind75 + some new problems in NeetCode150) and prepping behavioral questions by writing stories that mapped to multiple LPs. Having 4-5 stories mapped to a few LPs each will be fine. I had followed the STAR format as mentioned in Amazon's prep materials.

Data Science Intern:

Timeline:
Applied - not sure, probably Dec-Feb sometime
VO - March 3rd Week
Decision - 3rd day after VO

Interview experience:
I had 2 rounds back to back on the same day. I was interviewing with the team that would hire me. The first round was completely about LP. That's 1hr of LP. The 2nd round covered things about my resume, end-to-end workflow of one of my most complex projects, some ML theory and fundamentals, follow-ups about the project I explained, 3 SQL queries (1 + 2 follow-up), 1 simple coding question, and finally 2 LP questions.

The ML theory was just fundamentals; If you read and study daily, it will help you retain your knowledge. The fun part was the end-to-end project discussion. I was completely involved in explaining things, linking the business aspects and value with technical aspects and value, and how data science helped solve a real-world issue.

My prep:
For SQL, I just practiced SQL 50 on leetcode every day. I already had a good grip on SQL given my previous semester's coursework, so it wasn't a problem. I didn't touch leetcode for DSA and LP because, well, I had already prepped for SDE VO. I read a few books for ML theory, and wrote down notes about my projects (work ex. and personal projects), connected all dots, and wrote deep notes for everything, and read them once a day.

Finally, on the 3rd day after my DS VO, I got an email from a recruiter thanking me for interviewing for both roles, and that the team wanted to move ahead with the DS role. I happily accepted it, as DS was my top choice :)

LP prep materials:
https://assets.aboutamazon.com/d4/9b/6d5662ec4a75961ae78c473e7d03/amazon-leadership-principles-070621-us.pdf
https://igotanoffer.com/blogs/tech/amazon-behavioral-interview

ML prep:
Just a lot of Google searching and reading blogs every day

Feel free to ask me any questions, I'll try to answer them!

Has anyone interviewed for a data science internship on Amazon?

I’ve Googled and only found one experience that had leetcode easy with some previous project questions.

I’m wondering how in depth the questions are. I’ve been doing hackerrank/LC and studying a bit but am curious to know if I should concentrate on a certain area - thanks!

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.