I’ll be starting at Apple in July once I graduate, working as a data scientist in the Services group in Cupertino.
This is my first job so I wanted to know from current FAANGers - what’s it like? What should I expect?
Also what should I do these next 6 months to best prepare for the start of my career?
Any tips or advice would be greatly appreciated!
TC: 140k base /110k RSU / 15k signing
- Apple zbgsAs someone who lives halfway across the globe I grow up from, I would disagree that fb is a net negative for the society. Without it, I would never be able to keep up with many of my old friends and even celebrate their marriage, childbirth etc. (of course, it also comes with all the negative news like chronic diseases and people passing away etc.)
Tldr, it must be great never growing out of the bubble.
Disclaimer, I works at fb now.Nov 291
- To say that FB is better is laughable! I don’t think FB is terrible or anything but just look at its product. One creates hardware and software that allows for FB to sell its product which is you. Apple could survive without FB, I’m not entirely sure that reversing that statement would be true.
I work at Apple and just as every other company it’s got its pros/cons. I wouldn’t be ashamed to work at FB, after all its just a flipping job. But I personally like the OP would ultimately rather work in a place where I believe in the product as good versus just a connection platform.
FB really does need to clean up some of its privacy stuff or at least call a spade a spade and say, yes we sell your data, that’s how we bring this “free” product to you, you stupid simpletons. We anonymize your data but still sell you as person A to companies who want to sell you stuff. Period, end of story. Instead they try to act like you are not the product. So stupid.Dec 12
- You know most of FB users are on Android not iOS. Most of the world can't afford overpriced iPhones. And FB didn't even start on mobile. So FB can definitely survive without Apple. Also FB absolutely doesn't sell anything, they provide better targeting to advertisers no info about the users gets to them.Dec 11
- It’s the issue of clusters of like minded individuals being targeted by advertisers on a massive online friendship network that is the problem. Maybe you don’t have the background to understand the negative implications, but I can assure you they are profoundly bad. I have been approached by FB several times to do DS work but would never consider doing it.14h1
- Like I said you don't have an argument. You are the one making a controversial statement, so the onus is on you to provide an argument not for me to provide an argument for your own statement dummy. You seem bitter that you can't get into FB. Maybe if you spend more time doing Leetcode and less time making baseless arguments on Blind, you may actually have a shot.4h0
- Microsoft do_needfulIt sucks being a data scientist a.k.a. a data monkey. You should grow up to be an SDE.
- @tcyoegtfo maybe address the parent comment that calls data scientists “data monkeys”
It’s pretty clear you have no idea what data scientists do. I have a publication and two data science internships where I built and deployed ML models, ran A/B tests and presented recommendations to C-level execs based on statistical inference I ran. How many SWE interns have an opportunity to do that?
At the end of the day, a data scientist that can code & build ML models is going to outpace a one-trick SWE who jumps on the latest Front end technology fads
- New atmmYoung kid publish paper and think his dick is so big. Kid, after publishing so many papers, you only got your paper because you were in the right place at the right time. Granted you have shown you are not incompetent but don't let that go to your head.
Also you said you had a real data scientist job interview but no leetcode question, how is that possible, especially at FAANG????
And you present to C-level execs as an intern... Sure.. is this at a startup where their director of data science is a one man team?Nov 286
- Microsoft 🐙M🐙Enjoy your time instead of focusing on preparing too much. A few lessons learned quickly after joining a company:
- School work was much harder than real work but 40+ hrs a week eats up free time in a way that school never did
- Most things you learn in school are pretty irrelevant once you start work, as are things like your GPA and test scores
- You’ll never have summer break again so enjoy it
- Lol, after all that you want to spend your last 6 months preparing?
How about have some fun or travel?
- Im personally not a big fan of travelling, I’ve done it a couple of times (Australia, China, Spain, etc. ) and it gets old quick.
I can’t have fun, I live in a college town and all my friends graduated last year (I did a 5th year because I took a year off to do research in a lab)
- Ok. Let me throw in my opinion to some comments above regarding data scientist vs SWE. I am an ML PhD and working as a data scientist in non-FAANG.
1. DS pays pretty well. I think only FANG + unicorns can exceed my base pay and that too not more than by $10K
2. Working on your own to find patterns in data to show them to your supervisor. So in a way, you get to make your own decisions. I don’t like this because sometimes I have no direction. This will happen if you don’t understand the business model.
3. That’s it. Sorry 😄
1. I have not implemented anything production level. In my PhD too, I liked implementing things. I like the software side but I am not good at it (i.e. I am not yet good at leetcoding, that’s another post anyway 😄)
2. As people say, DS pretty much spend their time to find patterns in big data using HIVE, Spark and SQL. In simple words, find out how to increase revenue and tell that to managers. Then they tell it to the engineers where they build the necessary frontend/backend. Yeah, not interested.
3. Attractive title. Tons of post online on how to become a DS. I don’t know why though. ML engineer is definitely better in my mind.
4. Relaxed job if your team is not generating enough revenue to be relevant. Because, your analysis anyway won’t impact on revenue. However, DS in Google ads team will be good. This is a disadvantage because I am not learning much.
My recommendation: keep trying for ML engineer.
- Interesting perspective, I like data science because it allows me to interact with business stakeholders as well. My ideal role is 70% coding/30% meetings & presentations which data science seems to fill.
Also MLE roles are super competitive and most companies won’t call you back unless you have a PhD (which I don’t). That’s why i went for data science but eventually I’d like to try my hand at MLE!
- Walmart.com $randomGuyI am afraid that 70% coding would be only to mine the petabytes of data. I feel implementing something is much more exciting.
However, being said that, congratulations on your job offer. Go out there and if you like it, move up on the same track (e.g. senior, staff DS) or else you will have a big advantage with the Apple badge on your resume when you apply for MLE. Also, MLE doesn’t require PhD. All it needs is good leetcoding skills and decent ML knowledge. So keep working on them while you are at Apple.
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- What is the official title of your role on the careers site? Just data scientist?
- Thanks. Trying to assess whether it’s an ML specific role that I would need publications to apply for. I have the skill set but no industry experience.
btw, I would spend the time learning new stuff. Once you hit work it’s very hard to cycle in time to learn brand new things because you’re focusing on moving the needle on work projects.
- My role has ML elements but I’m not a core ML engineer, also expected to know spark, SQL, deal with biz stakeholders, etc. I had a publication in undergrad but I don’t think it mattered too much.
I’m sure you’ll have no issues getting an interview with google on your resume!
Yeah that’s likely what I’ll be doing. I haven’t had too much experience with Spark so will be learning that along with it’s ML library