Data Science: Reality vs Expectations ($100k+ Starting Salary 2018)
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Data Science: Reality vs Expectations ($100k+ Starting Salary 2018)


I get paid a hundred thousand base salary like I’m hired as a new grad Hey guys welcome to this episode of Reality vs Expectations where I get people from different careers and I have them write about five cards of what they thought their career was gonna be like versus what it’s actually like today I got Joma currently his a Data Scientist at Facebook previously he has worked at LinkedIn Buzzfeed and Microsoft he graduated from the University of Waterloo with the degree in Computer Science lets get this video started Hey guys today I got Joma he works as a Data Scientist at Facebook but Joma can you tell me the story about how you became a Data Scientist? Yeah sure so I did a lot of internships when I was in college I did some Software Engineering internships and I also did a Data Scientist internship
at Facebook and then after that I worked fulltime at Buzzfeed as a Data Scientist and then finally I came to Facebook as a Data Scientist can you walk me through a day in your life as a Data Scientist? Yeah sure so mostly what we do is we come into work and then usually we have a lot of meetings because we have to talk about what are like the next goals and what metrics to track for our team so for example I work for videos at Facebook and basically what I do is I query a lot of querries getting some data to try to make decisions for the PMs give me an example of a specific type of data that you’re looking for yeah sure like for example you wanna see alright what countries are doing like most well for our shows cuz now we have a watch tab on Facebook and we wanna know which countries are doing the best and which countries should we invest in and that’s one way to look at it and how did you become qualified to get a Data Science job? yeah so It’s actually a lot of different background you could come from a lot of different backgrounds my background is in Computer Science so with the Computer Science degree I was a little bit more technical and then when I did the internship because the internship they allow anyone to get it you don’t need to be technical and that’s where I learn how to do Data stuff like for example basic stats did you come from like extremely qualified school? is that why your looking for internship? I went to University of Waterloo which is a Canadian school and they do a lot of internships I wouldn’t say its like the best school in the world like its not ideally at all but we do a lot of internships and maybe that’s why we get more preference over other schools okay so lets go to the Reality vs Expectations questions what’s your first card? so my first card is most people think you need a Ph.D to be a Data Scientist and that’s actually a myth because I don’t have a Ph.D I should just have a bachelor and then at Facebook I’ve met many people that have the Neuro Schience degree or even someone that had a Family and Sexuality degree and most people come from consulting backgrounds so yeah so you deffinitely do not need a Ph.D I think the reason why peope are confuse by it it’s because when you think of Data Science you think of the machine learning of Data Scientist since they came from different backgrounds how did they teach themselves or how did they learn the skill well enough to get a job? so theres two ways either you do an internship or you just study basic stats because to be honest its less about learning the fundamentals or like being really good at the stats to be good at this Data Scientist you usually have to have more empathy to be good at Data Scientist because you have to ask the right questions and then answer them thoroughly because technically its not that hard you only need to know some sequel queries maybe a little bit of Python which everyone can learn what’s Reality vs Expectation card number two? yeah so this is a little bit related to the previous one most people think Data Scientist works so solely on machine learning and like artificial intelligence but that’s not true I just wanna talk about the three arc types of Data Scientist theres one is Data Science analytics? that’s what I am and then I’ll talk about that later and then there’s Data Engineers and then there’s also Data Science Core that’s what people at facebook calls so Data Science Analytics this is like us we just like a Data we do some sequel queries we process it we make graphs and we communicate with to the Product Managers and then Data Engineers those are the one that retrieves the data build the infrastructures so we can actually look at the data and then Data Science Core those people are like the hardcore Ph.D with like recommendation models in forecasting awesome, what’s card number three? yeah so card number three is Data Scientist is just about putting a bunch of data in a Blackbox model and then I would just output an answer so that’s very not true because what’s most important in Data Science is about you know like I said empathy and also understand what the real questions are I’ll give you an example why you can’t just put in a Blackbox now Blackbox what happens is you give it input and then you say what to optimize for now imagine you have a video product and you wanna focus on a specific country in the emerging market and then to help boost your video product and then what you wanna optimize is time spent it makes sense right because you wanna make people watch more videos and then you put in a Blackbox and it says oh Vietnam or Thailand is the best country but then that doesn’t tell you the whole story because what if the reason why they spent so much time it’s because their just spending time buffering or loading the video so that’s why you can’t just put things in the Blackbox cuz you have to understand exactly what’s happening to the users on the other side can you define Blackbox? yeah so Blackbox meaning like models that people pre create for example a simple linear regression or like a random forest or even like a deep learning model sometimes you can’t solve problems just by encoding data in these models so that’s why I mean by Blackbox It kinda relates to like everyone thinks correlation equals causasion like a two things correlate they think it’s causing obviously this Data Scientist you know better that just cause two things are correlating doesn’t mean that you know the causation exactly so one of the biggest mistakes is you know correlation vs causation and you will always find a correlation and then you would optimize on that certain thing thinking that it would benefit the other thing for example time spent correlates with likes for example and then what you see later on is that maybe if you increase time spent it doesn’t necessary mean the more likes you’ll get cuz maybe you’ll just get wasting time spent and stuff like that time spent there are lower quality exactly, what’s number 4? you need to know Hadoop Mapreduce and Spark if you wanna be a Data Scientist cuz these are like the buzzwords that you hear the most and that’s not true at all cuz I’ve never written a Mapreduce job in my life I have but not at my job and the reason for that is that usually the reason why you think you need these is because your applying to startups and startups they don’t have enough resources to hire the three arc types of Data Scientist so Mapreduce, Hadoop and all of these things those are usually the Data Engineers that work on this or Software Engineers cuz technically you don’t need to know much about data or statistics to create these pipelines so the Reality vs Expectations is that you don’t need to know these things when you thought you did yup so for example working at facebook especially because it’s so big they have three separate jobs for that you know they have the Data Science Analytics the Data Engineers and the Data Science Core so Data Engineers would do all that and you wouldn’t even need to think about it you can just focus on you know impact and thinking about how to you know how to make a product better with the Product Managers if someone wanna to get to Data Science today what website should they go to to learn more I personally don’t use any websites and I wouldn’t recommend websites I think you should definitely just try to get an internship and to be honest this a little bit harder but if you do have a technical background like computer science it would be better and if you still can’t do it maybe try to get a consulting job and then move into Data Science okay, what’s the last card? the better you are at statistics the better Data Scientist you’ll be yeah so what I mean by better at statistics we usually think about complicated models advance forecasting techniques and stuff like that I just wanna to tell you a little bit about what happen to my internship we had five interns one of them did some hardcore forecasting thing that’s like really complicated but the only thing it forcasted was for example the number of active users for that specific product and maybe it was very accurate but what is that give us for product what kind of like product recommendations does it give us it doesn’t really give us anything so in the end it’s not about how good your stats is or how technical you are value can you add to the company and that’s what matters the most because if you do many complicated things and like a lot of machine learning stuff but in the end your not giving any value to the company even if it’s so cool even if it’s like like really advance stuff it doesn’t matter cuz I can do the same thing with a simple logistic regression or like a linear regression as long as it has impact to the company then that’s what your valued at it’s interesting you are saying that it’s almost like working as a as a developer too it’s related that you can develop this complicated code but if it hasn’t have any functionality then there’s no point like it’s kinda like the difference between like doing something theoretical versus doing something practical theoretical can get so complicated but we can’t use it then what’s the point right exactly so I mean a lot more then often I see people over Engineer softwares and then yeah it’s really good and it’s marginalized but nobody can touch it because they just don’t understand how to use it and that’s useless alright Joma last question if you could go back to the beginning of your career your freshmen you just graduated from highschool would you do Data Science all over again? that’s a little bit of a hard question because I do enjoy Data Science now but I think there are somethings that I would like to do more I always wanted to be a Product Manager rather than a Data Scientist unfortunately like through this schooling that I had done I didn’t develop the skills as a Product Manager I develop the skill as a Data Science or as a Software Engineer so if I had to redo it I probobly would have focus more on like the business side of things what did you see in your professional career were now you would prefer to be a Product Manager than a Data Scientist yeah so I saw that Product Managers they focus more on execution and they also get more of the credit when things go well in terms of different products like in some sense a Data Scientist is like the right hand man of a Product Manager a Product Manager is like a mini CEO so I always love thinking about products and thinking about you know new and innovative way to think about you know how to reach users and how to make their lives better but usually it’s the PMs that have the final say I get paid a hundred thousand base salary like I’m hired as a new grad I get paid the minimum I get a hundred twenty thousand equity for four years that means like thirty thousand equity per year and then for the first year you get thirty thousand dollar bonus for the first year and then you’ll also get some random relocation bonus that’s worth like fifteen thousand or something like that I hope you enjoy that interview with Joma if Data Science sounds like a profession you might wanna start learning about consider taking this course I link in the description on Skillshare I’m your instructor Frank Kane and I spent over nine years at Amazon.com and IMDB.com developing and managing some of their most famous features like recommended for you and I think it’s a great introductory course and with Skillshare you get access to over eighteen thousand courses for fifteen dollars a month what a great deal and with that being said I’ll see you guys next week bye

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100 thoughts on “Data Science: Reality vs Expectations ($100k+ Starting Salary 2018)

  1. The first step is the hardest (expand for more info)

    Skillshare is the Netflix of online courses. Subscribe for $15 per month and get access to 18,000+ courses (some courses are better than others).

    If you use my link, you'll get 2 months free meaning you can complete the whole data science course and cancel before your trial is over.

    Skillshare might not like that, but I'm just being honest with you. Here's my link:
    http://engineeredtruth.com/skillshare-data-science

  2. Is that a specific program choice of data science? Or we have to get in the computer science first then be a data scientist

  3. This guy is a Data Scientist, he has knowledge of Machine Learning, its involved in his job, he's very technical (he's not an Excel baby). If its a duck then…

  4. Lol, one of his insights made no sense but the funny part is he said not to use websites and at the end you refer a websites

  5. hi sir JOMA I really like your video and I would like to jump into data scientist but I don't know exactly what to do and where to start thanks and I look forward to hearing from you back

  6. So I'm working on my Master's in Data Science from Texas Tech University. Do you think I'll land a 6 figure job after graduation?

  7. I am going into Engineering Management so I get to specialize more in the business analytics which is what you need to be a product manager. However, this is making me really enthusiastic about my future prospects. 🙂

  8. I'm probably considered "data science core." I have an MS in applied mathematics and I'm a good programmer. Most of my time is spent finding the best way to handle data. I call that "model selection." Models may be statistical, calculus based or machine learning oriented. After that I spend a lot of time collecting and verifying data. This is boring but very important. Third, I test the hell out of my models. I write a lot of technical documentation and try to make what I find make sense to non-technical people. Being able to communicate statistical, mathematical, business, and procedural information is extremely important. Just my thoughts. : )

  9. I'd second what others are saying.. that his job is more Analytics then Science. But, the lines are blurring between the two. I'm 40-something, quit my job in data infrastructure and analytics to go get my MIS. Schools are moving away from teaching infrastructure, and towards more analysis and science.

    My opinion (having sat through far too many analysis and research / science classes) …

    Analysis is basically Applied Science. The folks doing Data Analysis and Research have come up with applied methods to solve certain problems. So, you can do a market basket analysis of customer products to see what they're getting and use recommender systems to recommend others they may like, or text sentiment analysis on product reviews to see how customers feel about your products. This is stuff Data Science and Data Research has figured out, and has become so commonplace that it's now considered part of Analytics.

    Science is basically Research. Using techniques to see if there's ways to solve problems we dont' know about yet, or haven't been able to solve yet. This is stuff like… we have a genetic data set with some patients flagged as having cancer and others dont' have cancer… and we're going to toss it at some supervised learning algorithms to see if we can predict with some amount of accuracy and train an algorith.. then toss an unknown person at the algorithm to see if it can predict with good accuracy whether the unknown person will or won't have cancer. (the breast cancer data set is a go-to in data sci class). Or stuff like … we're going to feed in all the reviews from app stores, and see if we can determine which reviews are fake or not. Just stuff where it requires a lot of fiddling with the parameters and tons of exploration to solve the problem.. because the problem hasn't been solve yet.

    The thing is … data science was created mostly by big corporations letting their data analysts run wild with the data to try to explore and solve problems. But, colleges are starting to play catch-up by now offering courses (and hopefully degree paths) for data science.

    The goal is to have data scientists go explore problems we dont' know answers to yet.. then when they find solutions, they can tweak and optimize them.. and that rolls out as an applied method that analysts can use going forward.

    Data Science is also about taking old-school solutions that mathmaticians came up with way back when, and now applying them to problems since we have the computing power now. EG: the concept of neural nets have been around for a long time. But, we lacked the computing pwoer to pull them off. Now we do, and we have problems they can solve, so they've blown up as a big thing in Data Science. Data Scientists play around with things like that, and check the viability of using it to solve problems.

    Most data analysts at companies are not coding up neural networks to solve business problems.

    So, there's a distinction… but the lines are still a bit fuzzy and blurry.

    It doesn't help when companies and individuals want to give themsleves specific titles. When your'e working at a high-enough level to do BI Analytics and Data Science, the lines blur and you wear a lot of hats. You need to know data infrastructure / engineering.. you need to know analytics .. you need to know science / research to investigate problems. The ratios you do all of that seem to be the guiding determiners in whther you're a DBA, Analyst or Scientist. But, Each of those job roles does various mixes of all of that stuff. I can't think of any good DBA that wuold just let their DB fly without doing any kind of analysis on it.. nor researching other ways to make it work better. And analysts need to know how the DBA's are collecting data and storing it, how to pre-process it and such to make it useful for analysis. Scientists work further up the pipe with more analysis and exploration, but they still need to know how to organize / engineer data and do analysis work.

    Data is basically a field in and of itself… whether you work closer to the machine (infrastructure) or in the middle (info sys / analytics) or in the crow's nest looking out for opportunities (sci / research). And everyone in it is crossing paths every day and needs to know how each person does their job and how it impacts them to really be useful.

    tl;dr.. splitting hairs on Data Scientist vs. Data Analyst.. this guy can be called an Analyst, but if he's doing exploratory research of data to find new ways to leverage it.. then that's science. And, a lot of analytics work these days is about exploration / research. So, we're all crossing the streams. If you're more interested on getting hung up on job titles then the work being done, then yuo're putting the cart before the horse.

  10. Hello Engineered Truth, I recently finished learning HTML5 and am learning CSS now. As some foresight, what math level is required for JavaScript? Thank you in advance.

  11. Thanx alot for doing this effort really very much appreciable.
    One thing I like to know about data science if I want to do data science course which online tutorial is valuable so that I can easily get job and I use my money on the right track.?
    Please help me in this.

  12. So basically a data scientist is about predicting where and why and how the user uses the product and the best way to get them to come back to use that product. For instance videos on Facebook.

    That’s super interesting.

  13. interesting. this is the reason i am thinking about getting my degree in econometrics. very technical but since it is derived from economics which is related to business it would have more in common with business than a computer science degree. is my rationality fine? i dont want to get a business degree because they are a lot worse paid and the job market is full of them

  14. if iam a instrumetation grad….what are the best things to do become an data scientist???….coz in our place,my teachers doesnt even have one clue about "what is data science??"…someone help me!!! plz

  15. I'm starting an internship at a big insurance company this week. I'm going to be working with a PhD student who's cooperating with the company on research into churn modelling. My job will be to facilitate communication between him an the Marketing Intelligence department at the company, so that his very theoretical research can be turned into real, tangible recommendations for the company.

    I wanted to ask if you have any tips on presenting research findings to marketing executives. Do I need to go into detail about the methodology? Do I need to provide justifications for our choices? Or should I focus just on providing recommendations and results, without explaining the technical side?

  16. 6 figure salaries for graduates are unheard of in the UK. Even after currency conversion it doesn't come close. Can someone please explain this?

  17. I truly enjoyed the interview. Good questions, good answers, it's really helpful and it's helping me a lot planning my career.
    Thanks!

  18. So you're a data analyst, the way I researched it and correct me If I'm wrong, is you have a data analyst, a data scientist and a machine learning engineer. Where a data analyst will just look at the data and create reports and suggestions (not much coding experience required) and a machine learning engineer who creates the software that collates the data (requiring extensive knowledge of coding and machine learning) and a data scientist is somewhere inbetween both

  19. Joma can transition into being a PM… PMs come from all sorts of backgrounds… and having a data analytics backgrounds is a HUGE skill necessary for being a good PM.

  20. 100k lol those salaries are just possible in U.S, in eastern europe for the same Company , same position you would get paid $10,000 a year lol 10 times less.
    Would you do it then?
    People in US are lucky to land a job there

  21. He keeps saying product what kind of product facebook sells it what am I missing call me dumb but what product fb makes

  22. If I have a technical degree, years of experience in my field, and learned the tools (like SQL, Python..) to do data science; why do I have to go for internship and lower my pay for that period of time. Is there a way to go straight to data science?

  23. JOMA TECH is not a DATA SCIENTIST, he’s a 1st STEP DATA ANALYST. DATA SCIENTIST IS THE BEST JOB !

  24. Mind you, 100k in silicon valley is probably equivalent to 50k in most cities. (Coming from a data scientist currently living in pittsburgh )

  25. So you still don't need a PhD, but most data scientist positions require a quantitative masters degree… I think a lot of people are confused in the comments.

  26. Note: $100k doesn't have the same buying power across the country. i.e. $100k in San francisco is $60k in Dallas tx. Make sure you take into account the cost of living where the job is located

  27. His work sounds more than data analyst. Also he is making that money because he is at Facebook and lives in san fran

  28. I'd be happy if I made 50k a year. I come from poverty and never even know what money feel is my hands.

  29. age 16 getting intrest in this field shall i move forward? can i able to get a job in good position in future..

  30. Just startup a good business and you can earn much more. We started a healthcare business in our local town and first year got over 250k from client payments alone. Healthcare is def #1 for sure.

  31. Waterloo is Canada's MIT, so I kinda had to stop there. If you get in and complete a tech degree there you generally won't have to try too hard to get a job in the field. Internships help a lot more than university name, but I don't think he's a good example of this given his background. Waterloo gets a lot of internships for their students because their name is powerful and employers want to hire their students. It's a cycle of sorts.

  32. Just to add to this video that the graduate in this video went to University of Waterloo, which is like the MIT of Canada.

  33. He said everything I already knew and expected… but it was nice hearing it from the horses mouth because there’s so many people who have tried to discourage me just because I don’t know python yet…. or because I don’t have a computer science degree… I have a business degree with a certificate in data analytics/modelling/administration.

  34. Can someone with a BA in Economics get into Data Science? R and Statistics are core courses of that program.

  35. What's up with the random snippets of web dev googling? What does it have to do with what Joma is talking about?

  36. 6:46 is what i envision a data scientist actually "doing" 70% or greater of their work life. do data scientists have back problems?

  37. Please redo this interview with a real data scientist, not a data analyst with an inflated ego

    This guy really said that the forecasting example was useless. If you think accurate forecasts of KPIs aren't extremely beneficial for corporations to have access to, just stop.

  38. Recruiter: So, what makes you qualified for the role of Data Scientist?

    Me: I have a degree in Family and Sexuality Studies.

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