How Much do Data Scientists Make? (Realistic numbers...not Facebook, not Google)

and so coming out of college i was making about 85 000 a year in the san francisco bay area which is a pretty expensive place to actually live and so my take-home pay was around four thousand dollars a month what's up everybody this is jay from interview query here new camera by the way so if i look a little bit clearer then you guys know why i dusted it off in my closet and this angle is really like okay much better where was i today i am talking about data science salaries exactly how much do data scientists actually make i've seen a lot of different salaries out there on glassdoor today it said a hundred and twelve thousand dollars mean and then on indeed the average was 120 000 on pay scale the average base salary was 95 000 and then on levels.fyi the median salary was 150k with a whopping 238 000 median salary in the san francisco bay area which i honestly don't believe maybe just out of straight up envy but also for the fact that i also submitted a fake salary on levels like fyi of four hundred thousand dollars and they accepted it so who knows but exactly how can we tell how much data scientists make definitely depends on the kind of company you're at today i'll go over my experience on how much i made as a data scientist because i think tracking career progression actually really matters when it comes to talking about salaries and talking about how much experience matters what kind of company that you're at the size of the company and just how much money that they're willing to pay you and then lastly we can talk about how much money actually matters in the scope of data science itself all right so how much did i exactly make in data science throughout my career right so i started at a college back in 2015 and i had two offers from two different companies one was from workday and they offered ninety five thousand dollars in base salary plus sixty thousand dollars over four years in terms of stock and so this turned out in terms of total compensation to a hundred and ten thousand dollars uh the role was actually not within data science which is actually why i didn't take it but the role was a software engineer in performance the other offer i actually had was from inflection and this is actually the role that i did take which was as a marketing analyst and they offered eighty five thousand dollars in terms of base salary plus five thousand dollars a bonus after you completed one year why did i choose one offer over the other and why did i take this 25 000 pay cut the work day offer was interesting because i interviewed out of college for the role and they didn't actually ask me any technical questions at the time 2015 leak code wasn't really around i also just hated algorithm questions in general and so when workday just didn't ask me any algorithm questions they like laughed around joked we talked about the projects i did and then they gave me an offer i was actually really happy about it and this is more money than i've ever seen before making six figures out of college was insane proposition to me but i ended up not taking it because in general i thought that if i took the role as a marketing analyst i'd have more opportunities to do better and actually transition into data science later on after a year or so because the actual role and my boss was very like interesting he had a lot to teach me it seemed like from our interview then i figured that i would just take that role instead and so coming out of college i was making about 85 000 a year in the san francisco bay area which is a pretty expensive place to actually live and so my take-home pay was around four thousand dollars a month actual amount of savings on four thousand dollars a month in the san francisco bay area is pretty light it's pretty minimal i didn't really actually have that much left over and so quickly i realized that this was probably like the minimum amount that i needed coming out of college but as a new grad generally you know when you haven't even seen money at all within college having this was a great opportunity anyway after working at inflection for around three or four months i figured out that the company just wasn't really for me i wasn't really learning a lot my boss told me that he was leaving in the first two weeks i was there i figured it was probably a good time for me to probably leave after a few months given that there wasn't really much for me to do there and i wasn't actually able to develop my skills and so i was kind of kicking myself over not taking the job at work day but eventually it all worked out because i then i started a job at jobber which some of you guys know and i worked there as a data scientist working on the recommendation algorithm and so i've talked a little bit about how i got that job before but essentially i actually found it on jobber they reached out came in for an interview and eventually they gave me an offer for eighty thousand dollars which was a five thousand dollar pay cut but with also point five percent equity and so with this point five percent equity i thought this would be pretty interesting because it was based on the valuation of the company at the time which was ten million dollars so they had raised a seed round and essentially they raised two million dollars on a 10 million dollar valuation and so with my 0.5 percent i was therefore entitled to about 50 000 over the course of four years at that current valuation with the possibility of it also going up later on and then additionally in the employment contract they mentioned that if they had raised a series a then my actual salary would go up to a hundred thousand dollars once that series a had actually gone through and so i was under the impression that i'd be one learning a ton so i'd be stupid not to take the job anyway because no one else even really wanted me and then two it was a good enough proposition in terms of comparing the amount of equity i would get with also the kind of lower base salary for being a seed stage startup i thought that was a fair kind of compensation for me at the time especially as like a new grad coming out of college without that much to learn but just being kind of scrappy and willing to work longer hours just to learn a lot and actually contribute to a company and ship code in production so that was jabber and that was actually basically the first six months of my career there and essentially after six months of working there we ended up getting acquired by monster generally how acquisitions work are that bigger company will you know consume a large a smaller company like jabber and monster was interested in java for their tinder for jobs type of app monster actually acquired driver for about 12.5 million dollars and so to me that was like pretty amazing i had worked there for only six months and also now all of my equity was now being accelerated and uh vested immediately which meant that i got paid out four years essentially of working there when i only worked there for six months and so that whole point five percent was gonna be turned into liquid cash for me and for everyone else especially for the founders who would make it out with millions with my point five percent equity i thought with that calculation i'd be getting approximately 62 000 right uh no that's not how it works right and so essentially when a bigger company acquires a smaller company essentially there's lawyers that help out with some of the fees there's brokers that work with selling the company there's a bunch of other lawyers that come in again and take the fees there's investor liquidity preferences where they take more money out because they're guaranteed multiple of revenue when there's a sale and so at the end of the day what actually came into my pocket pre-tax was around 40 000 from that sale and so you can expect from any kind of acquisition that maybe like a third of that amount you're originally supposed to get even if it goes for above the valuation of the company that they raised goes into the hands of a bunch of different people you know greasing the wheels i don't know it's above my pay grade but in general this was a ton of money for me just like one year out of college in 2016 to get like a 40 000 a bonus essentially for working there for six months and so i really felt fortunate at the time i felt super lucky just to be able to join a company for six months and get out with an acquisition under my belt also i got a raise to about a hundred and ten thousand dollars afterwards and base salary with the added bonus making my total compensation at 150 000 per year with that amount it was definitely life changing entering six figures for the first time was crazy to me because just a year prior to that i was making about half that much at my former company and so can't stress about how much it actually matters to maybe one get lucky but also two to focus on developing your skills because ultimately that's what you're being hired for and even if you're not making money initially out of school or making that much in a position you got to have to remember to set yourself up to succeed in the environment that you're actually in so that you can further develop those skills and make more money later on in life so after the acquisition essentially we were also given an earn out ad dropper so an earn out is effectively when the larger company acquires a smaller company they build out these revenue targets or different kinds of metrics that you have to hit and which then you can actually get more of that money from the acquisition as well every single year after the first year that we got acquired on my contract i was able to receive up to forty to fifty thousand dollars depending on which kind of metrics and earn out kind of targets we would hit uh we forecasted that we hit probably ninety percent of them and so my take home total compensation for every year after that if my base pay did not increase was going to be around 150 000 per year and so after that year in 2017 i had done about a year and a half at jobber at that point and a year after acquisition our whole company within jobber the startup actually negotiated a raise for ourselves from our parent company and so two years of experience and two years out of college i gotta bump up to 130 000 making my total gross pay at 170 000 for total compensation on my third year out of college in 2018 towards october you know september-ish i started interviewing everyone at jobberhead by that point kind of left the founders had left my co-founder interview query shane had also left and i was looking to leave as well because the original culture wasn't there anymore and so i started interviewing around i knew i probably wasn't going to get the same amount of like total compensation that i had had previously at 170 000 unless i interviewed at bigger companies and so i initially just really wanted to focus on valuing culture and also valuing total compensation as well but ultimately finding like a company that would suit most of my needs within that time i actually signed up for hired and next door the company i ended up working for actually reached out on hired after i set my base salary that i was expecting to receive now that i've actually learned a lot more about negotiation a lot more about data science interviewing i don't actually recommend using hired because of the fact that if you do set your salary at a specific threshold then a lot of times companies will use that when they're negotiating with you in terms of making you probably setting a salary expectation that's lower than what they're actually able to give you right for your amount of years of experience and so many times if you go into an interview you want to withhold what your salary expectations are until the end so that when they give you the first offer it might be bigger and better than anything that you ever expected hired is still a great place to try to get more and more jobs also get just more incoming kind of offers when you don't actually want to go out and apply for you know x number of jobs while you're interviewing at nextdoor i went through the process i went on the on-site interview i had a great time meeting everyone so i really liked it and they gave me an offer of actually 145 000 which was lower than what i was expecting since i actually put it in my hired profile at 150 and so with that i knew that basically their whole goal was to bring your expectations lower so that you can negotiate up and so i ended up negotiating up to 155 000 with a 20k sign on bonus bringing my total compensation for that year to 175 thousand dollars in addition to that i did get some equity as well but it's harder to count that because a lot of the times the financing around that is very difficult for a long-term unicorn like next door when you don't really know when they're gonna exit even though there is a secondary market as well ultimately after i worked there for a year i ended up quitting to join interview query where i'm making zero dollars or not really zero dollars but definitely less than 175 thousand dollars a year i kind of showed that at that progression in my career if i had looked for another job again i probably would have been able to reach a level of 190 000 maybe 200 000 if it was facebook if it was google or amazon then potentially even more given how much data scientists were making within those levels and so i'd say there's a couple rules and there's a couple of things that you should think about when you're getting all of these like different numbers different salaries and to try to really understand when you're in the negotiation process and also when you're looking to break into data science and you see these salaries and you think that they're you know off the charts crazy right which retrospectively they really are so the first role that i have is what should you do with the salary information rule number one is to never compare yourself with other people right that's a recipe laden with failure you can only really compare yourself to yourself previously right and i'd say that when you compare yourself with others you're doing unfair comparison in terms of what other people have done with their lives how they might have gone about it what their experiences might have been how lucky they might have been you know in this case i think in general if you think about how someone could be really dedicated their job love to work in data science software engineering works like 16 hours a day then maybe yeah they do deserve that you know 300 400k salary that they have rule number two is to definitely determine your own market value a couple tips for negotiations or just salary negotiations in general is to understand where your market value is you are essentially worth the amount that you can be replaced by anyone else in the field right so if you are doing let's say bi analysis work even though you're a data scientist then you can effectively be replaced as a bi analyst at a lower salary right and so it's imperative to actually understand what your role actually needs what you actually need to do to grow and to actually getting to that next level whether that's being a manager being a more technical lead being the business decision maker any of those things require upskilling yourself and so understanding what it takes to get to the next level is definitely important as you progress in your career rule number three is that you really do have to figure out how much money actually means to you as a whole a great example of this is that you know every single day when i went into work i didn't feel like i was walking out with you know a hundred or two hundred thousand dollars right or whatever that equivalent is in a per hour you really have to understand what your goals are right are you trying to retire early are you trying to be financially independent okay then you know work as hard as you can and then eventually you'll be able to retire when you're 35 with millions of dollars that's great are you trying to actually learn a lot are you trying to basically take control and join a startup where you know that the money's not going to be as good if that's the case then money shouldn't matter that much to you anyway right rule number four is that you definitely make more money with experience so a lot of the times when you're just sitting there also doing your job learning and suddenly you have 10 years of experience under your belt people will actually value that i mean it definitely depends on your interview if you can communicate how much value you can bring with your experience but i've definitely seen people with similar skills but one person with just more experience is just older earning a lot more money that's because at the end of the day you can't really quantify experience that well it is still our number one metric in terms of determining how much money someone really should make especially if you think about people that have experience from bigger tech companies or in general made more previously people don't really like to go down in salary right and so ultimately you do end up getting to a cap but experience definitely matters a ton and so just realize that when you break in there's going to be 10 more years of just making more and more money as long as you continue to get better and overcome challenges yourself and upskill yourself to become a better and better data scientist all right the last rule is that salaries you know total compensation definitely will not keep you in data science this is a reiteration of the other things that i've said before but almost always you know internal happiness what you actually do at your job the people that you work with totally will and so ultimately at the end of the day you know these are numbers they put food on the table they give you a house to live a place to actually live when i was a new grad i don't think i was any less happy than i am now or previously when i was making you know double amount of money i just felt like i was happier because i could just save a little bit of money and prepare for the future and ease some of that anxiety but in general it's not like my cost of living has changed dramatically it's not like i'm going out and drinking or eating double amount i mean actually well who is now with covet but you get my point and that generally you know your life will stay the same no matter what you're gonna go to work every day you're gonna go out of work every day you're gonna go hang out with your friends most of it keeps it the same i think more of it should be around what you do what you have passion of don't think about it too much just work hard and everything else will come eventually also with that note please let me know what kind of topics you guys want to hear about next time on youtube i'm running out of ideas to talk about i only have so much history in my life to dish out so please please leave a comment like and subscribe add basically what kind of things that you want to hear from me about data science and i will talk to all of you later

Channel: Data Science Jay

Views: 37,957

Keywords: data science business, interview query, data scientist, machine learning, machine learning engineer, data analytics job, data science interview, data science jobs, getting a data science job, new grad data science, nextdoor data scientist, glassdoor, data science interview questions, data science salary, data science salaries, FAANG salaries, indeed, indeed data science, how much money in data science, machine learning engineer salary, facebook data scientist

Id: mkPuR4SEzkw

Length: 18min 6sec (1086 seconds)

Published: Thu Sep 03 2020