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?

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!

My 2.5 year stint at Amazon ended this week and I wanted to write about my experience there, primarily as a personal reflection but also sharing hoping it might be an interesting read here.. also curious to hear few other experiences in other companies.

i came up with 5 points that I found were generally interesting looking back or where I learned something useful.

1. Working with non-technical stakeholders- about 70% of my interactions was with product/program teams. remember feeling overwhelmed in those initial onboarding 1:1s while being bombarded with acronyms and product jargon. it took me 2 months to get up to speed. one of the things you learn quickly is understanding their goal helps you do your job better.
   My first project was comparing the user experience for a new product that was under development to replace a legacy product, and the product team wanted to confirm that certain key metrics did favor the new product and reflect it’s intended benefits. Given my new-hire energy/naivete, I did lots of in-depth research (even bought Pearl’s causal inference book), spent weekends reading/thinking about it and finally drafted a publication-quality document detailing causal graphs, mediation modeling, hypothesis tests etc etc…. On the day, I go into the meeting expecting an invigorating discussion of my analysis.. only to see the PMs gloss over all that detail and move straight to discussing what the delta-metric meant for them. my action item from that meeting was to draft a 1-pager with key findings to distribute among leadership. I clearly remember my reaction after that meeting- that was it?

2. Leadership principles - Granted this is my first tech experience, but I always presumed a company’s marketing material is sufficiently decoupled from its daily operations to the point where the vision/mission/culture code doesn’t actually propagate to your desk. but leadership principles at amazon are genuinely used as guide-markers for daily decision making. I would encounter an LP being the basis of a doc section, meeting discussion or piece of employee feedback almost every week. One benefit for example, is the template it provides for evaluating candidates after job interviews.

3. Writing is greatly valued practice at Amazon, and considered a forcing function for clarity of thought. I saw the benefits from writing my own docs but more so in reading other people’s docs. its also way more efficient by allowing multiple threads of comments/feedback to happen in parallel during the reading session vs a QnA session with a few people hogging all the time. On a related note, i wondered on multiple occasions how senior execs enjoy their work given all they do is read docs all day with super-human efficiency (not that they read the whole doc of-course but still..).

4. self-marketing and finding good projects - this was one of those vague truths that nobody will tell you but everyone slowly realizes esp in big companies, or atleast was true in my case. Every person needs to look after their own career progression by finding good projects, surround themselves with the right people (starting with manager) and of-course deliver the actual work. it might be easy to only focus on 3 believing 1 and 2 are out of control but i feel they’re equally important. example- one of my active contribution areas was for a product that, somewhere along the way, got pushed to a sister org, but I was wedged deep into the inner-workings that they had me continue working on it throughout my time. At the time, I felt important to be irreplaceable but what it really meant was that this work was not aligned with MY org's goals. doh! guess which org’s metrics will mean more to your perf review panel come the end of the year.

5. more projects are self-initiated than i realized. piggy-backing on the previous point about good projects- there is lesser well-thought-through strategy around you than it seems but also more opportunity to find the projects that interest you with potential for outsized impact. example- my most impactful project was a self-initiated one launched to production with a definitively large impact on the product metrics... and it didn't begin as an ‘over-the-line’ item (i.e. planned in the quarterly planning cycle) with a dedicated PM, roadmaps etc. it was just me finding an inefficiency and building a solution and even got it published in an internal conference. this may not be ideal but shows its possible to find areas for impact.
   I also know of at-least 2 other self-initiated projects that evolved to be core to the org’s efforts. This aligns with why companies hold hackathons, google has its 20%-time allowance etc. it also makes you wonder, how much of the OKR, OP, 3YAP etc are actually driving innovation vs designed to create an artificial sense of planning. (jargon expansion- objective key results, operational planning, 3 year action plan)

that's it. for me, this was a rewarding experience and grateful for the people I got to work with. I hope some of this useful to some of you folks, especially to junior data scientists, or an interesting read at the least.

I plan to continue writing and building my portfolio, learning full-stack web dev and learn some other skills (like marketing). follow me on twitter (https://twitter.com/sangyh2) if interested :)

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 got a verbal offer from Amazon for Applied Scientist L5. I have 8 years of experience after my PhD, and I was clear with the recruiter that I only interview for L6, and I think I did pretty well in my interviews. I understand that the level is based on the performance in the interviews, and I know that tech companies love to down-level, but I'm bummed about L6 -> L5 thing.

Has anybody here been successful to negotiate with Amazon to up-level after receiving the initial offer?

I have an offer from Amazon for an Applied Scientist role (heavy on NLP and ML) and a Data Scientist role at Google. The salaries are pretty close if justified by the location. My Ph.D. background is in EE with a focus on developing an ML solution.

I have a few questions from blinders that would highly help me making a decision.

Which one of the two (DS or AS) I can expect to grow more in terms of TC and career opportunity? I like the Applied Scientist job description more but Google is also a great company and has a great WLB. I might be easily burnt out at Amazon.

Which one I can expect to have a more challenging/rewarding job?
I appreciate any advice.

Hey everyone, I have an upcoming online assessment for a Junior Data Scientist role, and I'm wondering what to expect. It's not a Software Engineer I or II position, but the job description does ask whether I’ve taken a DSA (Data Structures & Algorithms) class.

Does this mean the test will have DSA-style coding problems (like leetcode-style questions), or will it focus more on SQL, statistics, machine learning, or Python-based data science questions? Or it will have those work style and decision making questions where I have to rank things, interpret emails and make decisions.

If anyone has taken a similar assessment, I’d really appreciate any insights on what was covered and what level of difficulty to expect. Thanks!

I graduate with a BSCS in a month, and I have an offer from Amazon as a full-time SDE. I really want to get into data science and machine learning, and I know that that specialty is better suited for people with graduate degrees (at least MS). I applied to a few PhD programs, but I only got into some very strong MS programs, which I will need to turn down because I can't afford to move and take on huge amounts of debt right now. My long-term plan was to work at Amazon and try to work my way into my area of interest, and perhaps go back to school in a few years.

I also just got an offer from a local company. They are a legitimate fast-growing company in a strong sector. They have basically offered me a job as a lead data scientist, in charge of designing and deploying a predictive analytics backend for their products (they bring in a ton of data). My concern is that I don't know if I'm qualified yet to lead a team to deliver something like this. I do have a pretty good background in machine learning for only having a B.S. (a couple publications in the pipeline, graduate courses in ML, data mining, statistics, distributed systems, etc.), but I'm a bit nervous about jumping into the deep end like this.

What do you think? If I go to Amazon, I don't know if I can work into the kind of role that I want without having the credentials. But it's Amazon. On the other hand, at this smaller company I can jump right into what I want, but I'm not sure if I'm getting in over my head.

Advice is definitely appreciated!

Hello everyone,

I’ve recently been offered a Data Scientist L4 New Grad role at Amazon, with the location set to Seattle. As a newcomer to the industry, I wanted to reach out to see what a reasonable base salary would be for this position. I want to ensure I’m asking for a competitive salary based on the market and my level of experience.

Any insights or advice on what salary range I should be aiming for would be greatly appreciated!

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

I have a PhD in Engineering and have very good knowledge of Python, SQL, and machine learning.

Currently, work as a data scientist in an insurance company (less than 1 year of job experience), but my plan is to get into Amazon or Meta as a data scientist as the next step.

My current data scientist position is mainly about data cleaning, building, and improving ML models using Python.

I do not have that much experience in Cloud and Big Data frameworks such as Spark, and my current employer does not provide such possibilities either.

My plan is to learn cloud (AWS or GCP) and focus on Leet Code for this. I consider 12 months for improving my resume and boosting the required skills. Considering my knowledge in SQL, Python, and ML, do you think improving my knowledge/experience in Cloud and Leet Code is a good package for a job change to Amazon or Meta? Do you recommend any other skillset such as Spark, etc?

Thank you so much!

Hi, I have a PhD in engineering and currently working as a data scientist in the insurance industry. My plan is to get into a data scientist position at Amazon. But I do not see many people with a transition from insurance to FAANG, while there are many from Banking and retailers.

1- Do you see working as DS in insurance isn't that impactful to getting into faang DS role?

2- Meanwhile, do you think gaining other industry experience than insurance can help me to reach my target (DS at Amazon)?

Hello all,

I am a Data Scientist at a Fortune 500 company, with a PhD in Electrical Engineering. For the last 5 years, I thought myself Python and Data Science and progressed a lot in that arena. I wanted some change after 5 years in the same company and wanted to explore options. Amazon AWS Pro Serve sounded interesting as you get to work with different companies. I did not want to work on a deep Machine Learning Problem on my cubicle (after corona, for now home desk :) ). I was excited about meeting new people and potentially solving data problems for different industries.

Am I making a right choice? Is Pro-serve considered to be same with, say "Applied scientist" role in AWS (asking in regards to: 1) career growth, 2) reputation and 3) financial) ? Is meeting new people and potentially gaining more exposure to different industries in Pro-serve a naive way of thinking? As we all know customer's behaviors can vary.

All in all I thought, exposing myself to different people and industries, in the future can help me even become an independent consultant, yes? Whereas If I am an applied scientist, I need to be deeply involved in "creating the AWS tools from scratch" vs using them in the AWS Pro-serve role.

If you have any experience with AWS Pro-serve data scientist roles, please chime in. Thanks in advance.

Edit: Also I want to mention the bad work/life balance rumors AWS has. I, for some reason, assumed AWS pro-serve may be different, and hopefully more balanced. But I do not have enough evidence to say neither for sure.

Edit2: thank you for the comments, I am blown away by your help and willingness to share experiences.

I have Amazon and Walmart DS internship offers. Amazon is def the bigger brand, is giving slightly more pay (~$2k per month). Both are in the same location, so that is not a factor. However, after talking to people working at Amazon I have been hearing that getting a return offer from Amazon is going to be next to impossible this time as they had over hired in the past. I haven't been able to get information about Walmart's chances of return offer. Also, return offers depend heavily on the team, and I haven't been assigned to any team yet for both companies. I was thinking of going ahead with Amazon and taking the risk of not getting a return offer. Because Amazon's a big brand I was thinking that I might be able to get a full-time somewhere, given I put in the effort for it. Is my decision of going ahead with Amazon and my reasoning for it correct? Requesting your guidance... Only here to learn :)

Please do not respond to this unless you are a past or current Amazon data scientist or you know someone who is.

I'm towards the end of the loop for a BI Data Scientist position (not Business Analyst or Data Analyst) at Amazon in Seattle recently after being at my current company for about a year. I've heard all of the typical Amazon horror stories but I've heard they're very team dependent. I've also heard you're not on call unless something breaks, and I don't see how building a model or doing an analysis would result in a middle-of-the-night emergency call. Does the same level of insane work/life balance that applies to AWS engineers and other software developers apply to analysis-focused data scientists? So far I have a good vibe from my interviewers. They seemed nice and asked good questions.

Glassdoor and Google don't have much information on being a Data Scientist at Amazon, and while I have friends there, none of them work specifically on a data science team. So here's hoping that some very niche people can help someone out. Thanks!

Year 1

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.