From City Tourism to Data Analyst at Spotify

hi everyone welcome back to another breaking into data session so i'm going to start just talking a little bit about uh breaking's data rotable who we are what we do and if you guys who are in the chat just please let me know if you can hear it oh perfect um i can see it now um but just confirm that that's that's working and then we'll be good to go so a few things before we get started um first introducing uh so i'm aaron fuloz founder promotable host breaking into data um my co-host scott adams um and then also really excited to jump into this conversation with rachel yes cool thank you um [Laughter] always always want to get that out of the way and i should have done that earlier but that's what it is uh happy to embarrass myself live um moving on um so what we do we bring uh every single week we have events like this where we bring uh you know practitioners who do data work every single day all right the whole goal here is for everyone who's you know either looking to break into data or level up in the data space you know let's talk about what we what we do and we're here to help you help you get there and with that i'm so quickly sharing my screen for a second a little short deck to help explain it all and then let's do present here we go um so yeah so basically you know breaking the data who are we work community where accomplished data professionals share knowledge and advice to inspire the next generation of data talent so we do this every single week today we've got rachel next week we've got someone from linkedin we've got folks from amex really just the goal is to talk about what kinds of roles you have in data how to break in uh what's the difference between data analytics data science right uh and everything in between um and so you know what we do of course we have our you know free events we encourage you to come every week we encourage you to ask questions um if you have them feel free to put them in the chat now um or as they come up um this is an opportunity to talk to someone who's been there who wants to mentor and help you so it's really an opportunity um next thing for those of you who want to dive deeper um if you are on the non-technical side if you're in marketing or product or finance and you kind of want to go beyond excel and you know add data to your toolkit uh we have a part-time course um just six weeks five hours a week it's all live online it's all taught by practitioners just like our guest speakers um we teach things like sql tableau and really just how to apply that to the stuff that you do it's five hours a week in the evening and of course it's recorded you get a certificate it's live instruction but you can also re-watch it later for those of you who are interested feel free to go to website motable.io of course next cohort starts in november or feel free to add me on linkedin and we'll um have your chat and tell you a little bit more if it's a good fit for you um and the second thing this is launching um next year at the end of q1 but really you know this is for people who are um you're you've completed data science boot camp or data boot camp or a master's degree or phd and you're trying to figure out how do you you know use this these skills that you have and break into data get that you know first data role um so we've put together a package that have you know gives you all the resources that you you might not have had from you know your previous training so course and network uh are mentors who are all practitioners you know it's vps of data science you know data science managers people are new to analytics ultimately there are things you can only get from someone who does it every single day and of course we have our own in-house technical recruiter so we like that more than the career coach because they spend all day long pitching companies on people like you and so you want to understand how do you make yourself look the best and last thing is of course we connect every single person who's a part of this fellowship with our corporate partners these are fortune 1000 companies and high growth startups who are looking to hire data talent of course this is uh there's no cost until you actually land a job and we actually mean that because the way i see it our success is your success and vice versa so for those of you who are interested uh definitely come and ask and we'll share more details as that gets ready to go in the meantime we can close that off and we're back so uh uh after that um so rachel um you know i know we we talked a bit before uh but i'm really excited to uh bring this bring this forward um tell us a little bit about you know who you are what you do um and then we'll dive into some of your background boot camp stuff and and then we'll we'll jump into other people's questions well hi thanks for having me i am obviously as you know i'm a data analyst at spotify besides that i am i'm in new york city i'm dominican and cuban for those that were wondering and i have a son so you'll see him walking around and i actually got into my journey i got into data analytics by taking boot camps particularly one from a sponsor by new york city called nyc tech talent pipeline and that's sort of like the gist of my path very cool so i know you did a couple of boot camps i know most people at least maybe i have no pun intended for data nerds but maybe sample error but i think many will usually do do a boot camp um so i'm kind of curious um kind of like how they accomplish each other or kind of what what like the main benefit of doing each one you know kind of talk us through and i think one thing to call out is um i i know there's a few folks that i know who went through the new york city um program so if possible i would love also just like a explain like what that is so that especially if we have anyone in the audience who's from new york uh it's probably something that they could consider sure so my first boot camp was was sponsored by the new york city so basically i up for my previous career in tourism i was laid off and i decided that i wanted to change careers and focus on tech and i found this program for unemployed new yorkers so the city pays to do workforce development and train people to for the talents of tomorrow right the high growth industries so they paid for the training it was a 40 hours a week 18 week boot camp on data analytics but envisioned they're taking people with zero tech skills essentially like they know how to use a computer but not no programming usual data analyst so it was a lot of it was basic right just setting the foundation and it was enough to start but i you don't know what you don't know in the beginning right so i was like oh i'll take a boot camp i'll be a data analyst great but then you start realizing all the things that you don't know that you need to learn and then also the realization comes that if you don't continue learning you're gonna get left behind so that's when i started looking and talking what will complement this program after so that's when i did more of a data science one i did two data science focus one and a quality assurance one after got it um i'm sorry i'm just uh playing with the format here this is much better sorry about that um no and and that's cool so i i think definitely like one difference that i've seen with like uh programs like yours where it's really like kind of zero to hero if you will um i i do want to dive into like what you do you don't know what you don't know because i think that's really important to call out both for people who are trying to break in and get their first thing but the reality is like it's something that i say all the time like anyone on my team like i have no idea if i'm doing something wrong i have no idea if you don't understand something and i promise i won't buy it you know i won't yell at you just raise your hand and tell me so i know but i think you know so many things you jump in and and and you figure it out um but i think there are i know for a lot of the bootcamps there's like some sort of you know test to to get into it um so there are some differences but um but yeah i know that's really cool tell me a little bit more about like you don't know what you don't know like what are the things that you thought kind of going into it what are the things that like afterwards you're like oh this is something that's you know there's something else i need to pick up or i had to learn this really fast or anything like that i think i almost replayed the same mindset i had when i went to college at first that oh you get a bachelor's you get a job everything's great you get paid really well and then i was like okay i'll take this program i'll become a data analyst great but then it became like oh it's not gonna be like that like they're just sending the foundation and i definitely had enough foundation that skills to work yeah but they've also shown me everything that i could learn that they just don't have the capacity there's no bandwidth in this program to learn i see so that was like the first like okay so i just got like i'm just scraping the surface on this model i'm scraping the surface and data analytics now what's next and then now that after they used to say they kicked the the bird out of the nest after i was done with that then it became okay what do i need to learn to grow one of those the more you learn the more you know the more you realize you don't know i know it's so crazy which is so painful um but no it's interesting so basically you did um the first program which was through the new york city thing i think it was branded under galvanized um for those who don't know it's one of the the bigger data science boot camps um you know there's galvanized flat iron metas um you know i think most of them are kind of like you get what you put in but you also have to understand what it means to actually do it it's like a lot of hard work but you know as as rachel can attest to there's lots of other gaps that you need to fill and and things that you need to also work out at the same time i assume that was kind of why you ended up doing the um other program as well kind of fill in some of those things that you realize that hey it'd be great to have this but we don't have time in these uh 18 weeks or what have you yes for sure like i just needed to know okay so we barely didn't touch machine learning at all so i needed to understand what is the foundation of that yes um but even then also like that my my first program you only need to know like basic excel and just like some competence in math and for in comparison their data science program in the same school you basically already needed to know a lot of the foundational concepts so a lot of the boot camps are stumble camps are meant for people to start from zero and the reality is they won't take you to data science right they'll take you to like just enough to get you into the industry data analytics and that was what that program was um and then now now i know that there are programs that take what i learned after the program so that now you could actually get into a solid data science path yeah no there's definitely things where you actually need you need to have like some sort of background and part of it's like trying and learning and messing up and coming back um there's a question on here that actually i think we should ask not good to address even now um you mentioned things that you don't know what you don't know um is there like obviously this is hard to pick one you know what's the biggest thing that you didn't know or just some just an example i mean maybe the biggest what i thought originally was that they were going to teach me a set of hard skills that i could just rehash over and over my entire career and that they told me basically by week one i realized that was not the case i was like i'm gonna learn sql i'm gonna learn python i'm gonna learn tableau and i'm gonna learn everything i need to know in that and move on but then after you graduate you start realizing like oh i don't even know sql that well i don't even know python that well or tableau because i just know like enough to get by yeah i know that that makes sense here's a question um to kind of diving into um uh maybe that the kind of work you do but i think part of it like the difference between someone who's in analytics versus like data science talk a little bit more about what like the if you can anyway like the kind of day-to-day or what kinds of stuff do that you do the data analyst sure so i work obviously for spotify and a big model for spotify is to help the user discover content and that's what i do i work on helping the user discover all the content that's in spotify because there's a lot of in there right you a user cannot possibly know every single like music track that we have so we do have to help the user to find out some similar artists similar tracks similar podcasts that you might be interested in and i work in in-app so you will do like banners or different like in-app messaging to recommend like oh check this out why don't you check this out that aligns with your interests so it's always just like about discovery and hopefully you discover an artist that you've never even heard before that's sort of like the the goal right to help you discover something that you didn't even know you never know the artist existed and now you love them what are some of the techniques that you use to accomplish that work so a lot of it is internal tools okay but um obviously once we set a lot of these initiatives my job is to create the audiences that go in and also to analyze how the results are going from these initiatives and then obviously get better at making those in the future make better decisions how much of your work is is just like cleaning bad data i always ask everyone because it's like a thing we it's like we talk about it a lot right bad data and bad data out but then i feel like we forget to talk about that when you're actually like going through programs it's like beautiful clean data sets from casual oh my god it is it is wild it also it's just so much data you never half the time i don't even know what data said to use so just like understanding the the data that's out there and then like it's gotten to the point that we basically have a de facto data engineer within our team who's had to take that responsibility to make sense of the data and transform it into something usable because even in a bitcoin like we're pretty established like we do take a really good care of our processes but there's always going to be yeah something unexpected oh yeah no i at this point i've asked the same question to so many people irrespective like big company small company you know super high tech company versus like old school industrial company it's like the same thing it's like no one no one's perfect but i'm always curious because like you know we always hear things like oh fifty percent of uh a data person's time is spent like either data wrangling or just understanding i think i want to point out um a point you made just like understanding what data you have it's not like you know like here's this question now i know i'm just going to look at this one thing and figure it out right you have to like what does that process look like for you is it like here's some stakeholder you know they ask you a question and you have to like architect the entire thing like how how does like i don't know if you can like dive into how that process works but um maybe at a super high level no well we do have our repository all data sets are which helps so we can search for data sets but even then i'm still like when i've been there only for a few months until there's so much data so i have to make educated guesses of where what tables probably exist like there's probably a table with a country name there's probably a table with x and then look to see if those samples exist and they look to see if they have common fields and there's still like unintended data cleaning which is like the table's not dirty but it's in a format that is hard to connect so you have to constantly like change the format of each field so that you can actually make connections yeah someone had a question about what internal tools are obviously you can't dive into like what exactly your internal tools are but i think what we can say is like generally speaking these are like internally built like sql editors and like this kind of stuff to allow people to look up you know any anything in your database so if you've worked for any sort of big company you probably had something like that whether you called the database or something else um but you know that's that's kind of what what that is yeah some of the like tools that people would know that we use a lot is we use bigquery um and we have to use bigquery and tableau we're starting to play with data studio but mostly we do our dashboards in tableau and we choose uh my team individually just python for doing a lot of analyzing but we're pretty open like some people use r some people use python which is becoming more of the status quo which you could use either are there things that you learned in your boot camp experience that you use on a daily basis in your job now that really stand out yes one of the things in terms of the python side we focus particularly in pandas and and and simple visualizations and that is something that i use basically every day pandas or something like super useful even if it comes to the point that i i just asked someone like i want to do this function and pain this cut and melt but i want to do it in tableau it's not a thing like it helps you even find it in other tools um so that's something i use all the time and then for sql even though we use i learned in postgraphs and i'm using bigquery now um there's the foundation also the same it's always like a google like how to do this thing in bigquery and find the matching thing to do um little things that are different that can throw you off uh yeah thank you sorry about that scott you you uh just got chopped up for a second there you go well that's probably a good thing because my cat was meowing at me so it didn't come through okay so you have a cat i have a dog and uh rachel has a a kid so maybe someone's you know this was gonna be something there's something complaining there's gonna be something um so yes everyone has this we're all real people um i always question someone who doesn't have that um but that's just my uh my soapbox to share but we're gonna we will dive into some more specific questions but we're getting uh the general theme is kind of like your post boot camp experience like how what were like the hardest things about like your interview process things that you that you maybe didn't realize coming out of your boot camp that after your first five or ten interviews you're like oh man i have to learn this one thing i don't know if you can share some insights on that kind of stuff oh yeah it's funny when i was in my boot camp i like couldn't wait to finish and then after you're finished you're like i want to go back because it's like you're alone you're kind of like don't know what to do um in terms of my experience i i made a good decision that didn't feel good at the time but i actually booked my first interview like a month before i graduated like i got at some point like oh i already know everything i need to know there's only a few months a few weeks left and it was a disaster just an absolute disaster so it became like my first reality check like oh i don't know what to do i i know the concepts but i almost didn't know how to put them together or i didn't know how someone else would verbalize them um yeah so that was like my first okay i need to look at these things in a different context in just my class yeah something that i did after i actually did just happen to do a big query like little like coursera course just because i i actually ended up having a bigquery interview at some point and it was live in bigquery so i think the interviews became almost like um reasons to learn at new things yeah a lot of the take-home exams are essentially like practices you know like small little projects yeah look um can you talk a little bit more about the take-homes and i i want to call at that point as well because i i think i've this is not the first time i've heard this we actually even last week speaker said the same thing it was like you know critics that were like interviews are an opportunity to um to learn something right to like learn something new about what you don't what you don't know um and he had recently um moved from like a smaller company to a much larger kind of company with you know he went from being a data science manager it's not more like an individual contributor but like you know a huge i don't think i think there's series b i don't know the phrase like 50 million something like that right so it's hard to play yourself a startup at that point you raised that kind of money um but one of the things i was asking about that with that process was because like it's one thing when like you don't you've never had a date a job and you're trying to get that first one but then i was like oh well now that you've done that is it you know really easy and he's like no just like it never never gets easier you're learning you're learning the whole time he's like there's all these things i knew but then i got into this next interview and it was like a total disaster and then i was like oh i need to fix that um i i i i appreciate that um but yeah so um going going to like that interview process um were like the most challenging parts the take home or is that easy uh it was like it was like a standard process for you was it kind of just like scrolling screen and take home and then some sort of on-site or yes i went through a few interviews uh funny enough it it i realized quickly then the take-homes actually weren't the challenge themselves they were challenging but they were more like time consuming because if you did a boot camp and have some foundational skill you you should at least like the ones that you you probably learned is how to google what you need so a lot of these take home exams it's like well i know i need to do something that does x find it so you you kind of i was able to figure them out the the challenge with those became the questions themselves the questions themselves could be like here's a data set tell me something interesting or here's the data center here's some really vague question that makes no sense and will give you no context so i will spend like eighty percent of the time just try to like get into the mindset of the interview like what are they trying to get um so it became like that was more challenging the take homes the technical ones were like more straightforward but the take homes it had to do with like digit uh like more of the analytics side like find out some insights answer this question those were much harder and time consuming i but you i got better at those as time went by what was harder to get used to was the actually verbalizing what you did what you could do applying the concepts because you just haven't done it um and i didn't realize that but i probably have like i don't know if i love the imposter syndrome concept but it's also like you feel like you're lying like you feel like i i'm clearly making this up like i've never done this uh i don't know i mean uh scott also has that has his fair share of interviews i don't know if you have anything else to tell one but i remember scott last week you made a similar point about like verbalizing the stuff that you're doing so that they kind of can follow along with your work i don't know if there's anything else you want to tag along on that point yeah it's a cliche that if you can teach something you can you really know it when you or more generally when you can explain something you really know it but i think that's a great point that you make that that is a really hard part and you think all about the hard skills and actually coding but looking back and being able to articulate the choices you made and why you made them in comparison to other alternatives that are available is a really tough task and something to be mindful of in the interview process so i appreciate you bringing that up so uh of course the standard question of like what was a joint like uh to kind of break into you know finally break into uh data and land a cool company like spotify but i think that maybe the better question that they might also be asking is like you know were you like hey i really want to work for this company and you kind of figured it out or is it like you did 25 interviews and kind of like step by step you know you kind of manage that opportunity but it wasn't like you didn't start day one saying i want this um if i'm being honest i started i did this boot camp when i was 30 i believe so now i'm 32. i felt that i was sort of like late to the game okay and then i needed to push like and trying to get my feet wet as fast as possible yeah so then i knew that i needed to be like a big enough company that had like a lot of data processes going on a lot of methods so that i can learn from so my first data on this job i was essentially the only data analyst there and i had a i was able to do the job but i didn't have anybody to learn from so i knew like from week one that it wasn't gonna last because i'm like i need the next thing yeah company that has the data structure yeah no i i feel you on that it's like um i think one um we talked about in previous events like the difference between being working at a smaller company versus a large company um i definitely uh i definitely feel your pain i've been in those kinds of roles where you feel like you're like making it up as you go along uh you're doing it but you're like i only want to ask a question there's no one that you can ask that question to except for google um and and um for those of you who haven't checked out let me google that for you um it is uh when someone asks you a question that they should be googling instead of asking you you send them this thing it sends them the link goes to google and it googles it for them um it's kind of funny i wouldn't recommend doing it to your boss um but um it's definitely my brand of humor um but no but i think that's that's really relevant there right trying to figure out okay let's go to a company that has like big established processes so you know so i'm sure you were targeting kind of a fortune 1000 or like really high growth startups or i guess spotify's public at this point so um companies that had a data team yeah and you could tell right just by looking at the job status they have a data scientist and a data engineer are they publishing research work like you kind of got a sense for that um obviously a lot of them became like more well-known companies or companies that were in a space that would require you to be utilizing data yeah here's a question um what you know on your team is it is it mostly is it mostly people that have like really strong like watch data of like degrees like phd type things or there's a lot of people who also um came out of boot camps or like a non-traditional um way of breaking in um it is a massive company so there's there's everything yeah i know obviously you can't speak for everyone but if there is like um looking at your team for example um is it you know kind of like is there a good mix or like you're the only boot camp person and if you can't share it's okay as well i i've come from a relatively small team so i believe i might be the only person that like their foundational knowledge was boot camp i think you find a lot in which the foundation knowledge is related even though it's not data science like we have someone who studies stats and they feel like they don't use most of what they learn but it is like a foundational topic or you have some people that went through the marketing team and now they're working in insights right so the marketing context was foundational for the data work that they're doing so ideally and i think that works outside of spotify like it doesn't matter it doesn't matter as much so you don't have a computer science or a data science background as much as whatever you did before other connects or in my case you almost like make it connect i feel like i almost like re-examined my past 10 years of a career from the lens of like all the times i was using data and what i did with it and then like in my mind i was like i've really been a data analyst for 10 years well i you know you're preaching to a choir here i always tell people i'm like it doesn't matter what you do you do data you might not think you do data but you're thinking you know even if you're like running a small business it's like you know i need to buy like this many pounds of this or i need to get like this margin or like whatever it's like you're not sitting there oh yeah yeah writing sql queries to figure it out but um data doesn't have to be huge data right it could just be like the end of the day like the most important thing is that the question part versus you know all the all the cool uh cool technologies um well we kind of just talked about that i'm just going through some of these questions someone just asked about your uh kind of like what that process was like kind of you know most these companies are like hey entry level role but you still have to have like two plus years of experience but um i i think it's you know feel free i mean i'd love your your take but i think um you kind of started talking about how you had like tie that together with like okay i've got this experience i've been doing data let's talk about that yeah like for context i used to work in the tourism sector and it was on the sales side so i used to go to trade shows to talk to teachers to bring their students to my the attractions i represented or i would work with resellers who will sell like like expedia to put our attractions in their website to bring their customers or even go to other countries to convince people to come to new york city so that was my job for 10 years and that's like the the most of the job or what people associate the job most with but to do that well you need to have a good operations because it becomes like you have to scale a lot of contracts you have this scale like people going into the door scale billing and that was the part that i liked so i was usually a team of a sale a whole bunch of sales managers and the only one who would say like let's like collect all of our accounts and see which ones are doing well or not yeah and nobody volunteers for that you know yeah let's understand like what our cost of acquisition is how long does it take someone to do this and that i'm also a operations guy so i think we're uh more more similar than different at this point but um because i think that's like that's the most i mean once you have a good business like that's the next most important part um someone was asking like i'm not sure if you can share this but like how complex do your queries get for like is it is it mostly kind of just like you look at some of the stuff that you have and you're like you know this is like a linear regression problem i could do it in excel if i wanted to um or is it are you mostly doing like i don't know ridiculously large queries so it's not about how complex it is they they get compostable and within reason because it's usually about aggregating uh aggregating different data sets together what becomes complex that i've i'm still learning it's because there's so much data i i'm still learning how to optimize it so i don't incur as much costs and that becomes like an even bigger challenge because you're like it looks simple enough and then my co-worker is like oh my god you can't do that that would be crazy i'm like i don't know what to do it seems like this is the simplest way so sometimes you have to make the query be more complex to so it ends up actually incurring less cost or it's less complex for a big query yeah it's it's it's interesting that um i've heard some like like the bigger like fang uh companies of big tech companies will say like we have a compute budget um and we can only like run so many things and we have to like um show the roi from like this model we ran or something like that based on like the amount of like server space we used and you know the data center into that or wherever they put it um we i have never heard that conversation internally but i know that those two exist like i could go in and check the cost of my own queries so i'm like it's public thought let's just just i don't want people to see this yeah that would be kind of cool um just as like a side note um but that's just because i'm a big nerd um but you know it'd be nice to say like oh yeah i solved this problem and by the way i did it with i you know i used way less way fewer resources than uh you know we use than like the average yeah that's the goal or the goal becomes like trying to search to see if somebody else already has a table that gets you like halfway there and then like be smart enough to like find that and use that table instead yeah no i'm definitely a big fan of work smarter not harder um but you know i you know it sounds like like you're trying to cheat but the reality is like i think you mentioned this point 15 minutes ago um when you said something along the lines of like okay well first it's i don't even know all the data that's there right first like let's figure out which tables we have let's understand what you know basis of like how this could potentially work kind of think that through um i think that works all the way around um but going back to um kind of like that like navigating the post boot camp what's next i love that you started doing interviews early because you learn very quickly i mean i remember my first interview is atrocious so embarrassing i'm not going to talk about it but it was like they asked me what was p value and it was just i'm so embarrassing what i said yeah no they're all no pun intended but they're all they're all data points right all the things that you learned um but one of the questions was um like uh how long does it take after after you're you're a few finished to like find a job you you liked but i i i think i'll add to that which is like how long did it take you to start getting comfortable in your interviews um because i think that's like how you get to like the whether you get the perfect job you like i mean no one gets the first like the first perfect job right it's kind of like this works now i know what i like now i can figure it out but um i think there's a point where you're like oh this is not as hard as it was earlier or now i'm comfortable talking about p values you know i think it took me let's say probably at least a month if not two to just start getting my narrative free boot camp better and connecting to what i was doing but the first like even the applying not even actually interviewing i kept like slacking my professor like can i do data cleaning can i do data transformation can i i didn't know what like the basic terms meant so also like i didn't even know what to apply for in the beginning um so it took me at least i would say like one to two months just to feel comfortable with like get into some of like the situational uh questions which i think would catch me more of guard um and then within i would say after i did like three or four like some of the take-home tech assessments then i got the gist of like okay i get what these are doing mm-hmm um so what about like quantity of them like okay after a few of those you get what they're doing after like my first like couple of case study ones then i was like okay i get i have a better handle of what they're looking for but it i have like a whole folder of them you you need to you should like just just apply and just get as many like i remember one that was like um we're calling you because we think you're a good candidate but also the truth is we offered it to someone but they haven't accepted it so you're kind of like our backup option like to see if it will work out if they don't accept and i was like that's fine i'll take the assessment yeah no i mean take all the assessments you can get right all the questions um yeah so uh what about are there other cause that's actually a really good point right take a record of like all the interviews you do all the take-homes are there any other like little things like that that kind of can help you kind of or help you know folks in the audience kind of take that take the next step but kind of um you know have more of a strategy to the interview process and that sort of thing i think you have to learn so you my bootcamp had like a lot of classes on how to manage your career your job search process and a lot of it had to do with networking right like making this relationships but i that's something that's like gave me a lot of anxiety um because like i didn't know how to sound uh authentic um i was intrusive now in retrospect now and i realized like it's not a big deal like i love them to reach out to me cold like it's fine but back then it felt uncomfortable so i would actually like not apply to things holding for that um and it took me like it took me a while to then realize like my anxiety over that is preventing me from applying and if i don't apply i'm clearly not gonna get jobs so then it became like a volume like i had to change i changed my strategy to more like a volume strategy but volume in terms of like i'm going to apply to every job i find interesting and if i get a grade if i don't get it great if i get moved to a stage at all then i feel like i've validated that i did something right in the application yeah stress so that i kind of had to change my strategy to match what my strengths were because i felt like i was better at the technical side once i had the interview then at this like chatting warming people up part yeah no it's it means i think part of it is just learning i mean from one introvert to another um i would say that i definitely understand like the fear of like not wanting to put yourself out there because you're afraid of like you know i'm gonna be embarrassed or do something stupid i mean i did like five stupid things just during this last 40 minutes so i've learned to just kind of let it go um but you know it's one of those things where i totally get it um i i i love that you pointed that out because it's like it's very easy to say very hard to do um but um one question here just came up and on the side no like i had zero connection in spotify like it was just cold apply that's really cool um speaking of connections um i want to talk a little bit about networking i know everyone has their way of doing it um is there like did you aside from just cold applying did you like set up like informational interviews with people or just like talk to people or um obviously like you know you probably learned more about that as you kind of got into your your interview process but i'm just i'd love just some insight for the folks here trying to figure out how to go about that process i did two things as mentioned like the the cold reaction i was not super comfortable but i was going to a lot of events and i used to like try to make sure i connected with every person i interviewed remember i went through an interview that i had 12 one for one job i had 12 people i interviewed with like in my mind those were 12 connections yeah um and then the the reason why i like i will go to events i want to nurture the connection wasn't part of it was almost selfish like i wanted to just hear them talk about data to know how i should be talking about data here the questions they would ask so i don't understand like the the mindset of people that are in that field yeah because it's almost like it is hard to talk data and com come off authentic in the beginning it feels like oh i meant to say this and i i meant to say like for example in pandas i meant the same method and i use function and like you start mixing up little things that um so that like i just needed to go to these events to hear people and that would help my comfort level and then like you know your you both went into the bathroom and it becomes like more casual comfortable and less force yeah yeah i mean or sometimes it does get forced i i always think of this time i went to i used to i used to go quite a few years back when we could do in person things and i i connected with like vps of like really big companies like one it was a marketing automation company um and it was like we were both like awkwardly avoiding talking to anyone and we're like you know uh oh over yeah we like we're both trying to grab the same brownie or something you know like um and it's it sounds so stupid but then it was like okay yeah you awkwardly like shake hands and you once if you can move past that at least we we did uh sometimes it works sometimes it doesn't but sometimes it's it's as little things like that just putting yourself out there um and like not trying to ask for a job yeah because obviously if i found your linkedin like i i have some information about you i know the company you work with i have an interest in the company most likely but in those events it's kind of like why are you here why why are you here why are you here like because the beer is free you know like um but but for real that's often times what it gets um what about for like practicing like assessments is there like any way that you you did aside from just like going to your interviews messing them up and then learning fixing that on the back end um are there any like resources that you use to like practice beforehand um besides no i was about to say that but um what i did do is i just happened to be a ta for the next cohort of yeah students so then within the next few months not only i had like the people that i graduated with and now the students every person that told me they went on an interview i asked for their take i asked to like either see their take-home assignment if i could some people will be like i share it but i can't send it to you or like them to describe to me what i did so i kept like having like this i kept trying to find out as much as i could about the possible things i could encounter in a take-home exam or you're in the interview really ringing smart so listen to like every person that is around you just be like every time you see an interview let me know how it was if you have a slack channel like post your interview experiences so we know not to commit the same mistake oh yeah no do we don't no no no one wants to do the same the same stupid stuff um i taught you a guard that you like didn't even know people asked yeah oh yeah no i mean it is i've done my fair share of like absolutely dumb things but um folks who are watching we've got time for a couple more questions um feel free to to throw them in when you when you get a chance um but yeah one thing i mean i i love i think definitely being a ta if you can i think it's super valuable um both from like the network perspective and also like for me i think it's kind of similar to like when i had my first manager job if you will and i started doing interviews and i started learning oh those those those are the things i say and they sound so stupid and so you like learn how to like not do it again yeah being a team is helpful and also like sometimes if you don't have the time commitment or you do you there's probably lesser ways to get involved like okay maybe you can be a team but maybe you could be a mentor in the project if you can or a men or even informally a mentor yeah yeah yeah no absolutely um any any other other kind of final um parting um oh here we go one more question um oh what was your process like for getting cold interviews um i i assume it's more luck than anything else but um um i just started every time i saw a job that i really wanted i rather than like what i was doing before like save it for later when i have more time to like work really hard on the cover letter i have like bear to the bones cover letter that i oh somebody actually gave me this advice sorry if it's not the same as promotable but like i had like this cover letter that was pretty small and they said like just the first two sentences it should be like a wow statement something that catches the person's attention as to why you want to work here and then the rest can be like pretty straightforward so like cash your attention with the cover letter and the rest you know it's okay if it's generic so i have that format so i'm like okay i just need to work on these two sentences and submit the application so i just submitted it and i got a call but i i realized that over time actually like i graduated in january 2020 so like maybe it was because the pandemic was like becoming more status quo but then it became like cold applications that got good response from them obviously not easy applies for cold applications obviously working on your resume like i have a lot of key a lot of keywords and things that are part of the job because i was doing more smaller courses so i was adding by nature of that a lot of like more technology that i was learning yeah yeah yeah and being able to like show it like that people always ask questions about projects and that's always like as long as it's not the most generic one as long as not your titanic data set um we're happy but um oh you pointed out this short cover letter i agree i hate reading cover letters um i i see like three or four lines and that's all i'm going to read and that's why i tell people the same thing now it's the same way when i reach out to people i reached out to you right it's like four lines um here's who i am here's what i want here's why it matters to you um that's it and then if your resume sucks then well you know yeah just enough to get the person to read your resume or vice versa exactly exactly it's like that's like half the battle is just like it's like did this person do basic amount of like reading to understand like what we do we do and like why they're fit like why they care it could just be like hey like you know i really like this industry i'm like a total nerd and i love this i love what you're doing cool mission like here's how i think i can help um it's like sometimes as easy as that um in terms of like um that we'll try and try and grab two of these questions here um in terms of you mentioned you did lots of small courses were there any like certifications that you recommend like is it is it important to become like a tableau wizard or whatever they call it um or like anything else that's like super important that like matters for a resume or is it just like stuff that's like helpful for you to learn but like the person interviewing you doesn't really care if you have like the tableau badge so i i did a linkedin course in tableau so i had like an essential linkedin certification so it was in my resume but i absolutely hate it like i really don't like doing it at all but in retrospect like i see it now that is one of like the most valuable skills on the data analyst level i guess particularly because a lot of data scientists don't have that skill set right and like it's almost like we have all this data but we can't see the content of it so like my team is becoming really big on like creating dashboards so i wish in retrospect that i would have spent the time to become certified in tableau i think it would have been like a game changer not necessarily for my resume because i had a certification but it was more like for my own comfort level now i'm sort of like learning on the go while i wish i would have been like okay this is really valuable let me really understand it yeah that makes sense i will end with this last question um i'll answer it with my opinion and then would love for you to jump in but someone just asked and particularly for spotify for like projects like what kinds of things you recommend if they recommend like doing some sort of like recommendation um engine or something like that i would say like you know will you understand like the kind of business that they do like if it's doing lots of optimizing if you want to work at an airline all they do is optimize right we're trying to figure out like how many people can they put on that plane with like maximum revenue with spotify it's like hey we want to make sure that we direct you to find these new artists or like other kinds of stuff so it's like you know generally speaking you want to have probably a couple of different projects specific to different industries um that's that's my personal opinion i will definitely underscore that as you know not definitely not like canon or like what you have to do by any means but i'm not sure how it worked for for you or like if you've been on interviews now um like when you're looking at other people's projects like um you know i think we'll end with your thoughts on this so i would say that i've had a few people uh in linkedin send me this message and i will always say do not do a project for spotify like i should be like you should not do that do a project that like you feel is going to help you for like the types of jobs that you're applying because what if the spotify like i hate the disappointment of you spend 20 hours for this project and spotify just to not get the spotify job and then feel like well i've made it so accustomed to them oh yeah you're right yeah so um so that's one i would say that i for me i didn't i had a lot in github a lot of smaller projects but most of them were from school i don't think i had like this like major marquee project that they looked at yeah like i think they just made the comment my boss will go i see that you're very active and get help and that you have like a lot of them have python so like it was just like enough to get a sense that i knew how to use python enough to put projects together yeah but it's because in my particular um interview they have a first to take a attack assessment and then a take-home assessment that is a case and the two data analyst shops that i got were both have the same component which is here's some data and then give me some insights here so i've had many of them but like these into a particular um or here's a question and what i realized on those that if you take really good care of those it's almost like your past projects are not as important as this one project that i am doing for you as part of this interview yeah so i like i literally spent hours just reading the spotify annual reports to get it in the mindset of like what was important for spotify as a public company they have to tell you what metrics they value they have to tell you the direction of the company in the annual report so i just looked through that asked to like get context as if i was working there and then when i did my case study then i one of the things that they really liked was that i was using almost like subconscious is the same metrics that they use important that they use because i have been studying as if i work there yeah no that's actually a really good point um super insightful actually you are the first person to have ever said that they at least for me that as long as i've been doing this i might actually went through a company's annual report i didn't read the entire thing you know but i mean you you it's smart right you go through you understand like the kinds of like jargon that they use we understand like how they're what they're telling investors hey this is where this business is trying to go because ultimately like look we we all like you know the only reason the company is able to pay the bills is because they have this business right like you know it is what it is but understanding that like have that domain knowledge if you will like um you know i i think it's really valuable i think it helps and i think for you it helped um yeah i mean like if you can speak the company's language that's like half the battle because at least they know they get why you're doing it and also to be honest it shows you did the work like it shows that you actually did that and that's like half the battles like you weren't told to you understood how to do it right all those things all those intangibles that like you can't you can i guess you can't teach them but like you know it's one of those things if you someone if you know someone does that and has the initiative um then they can teach you better sequel right it's like that's half battle um okay that's that's all the time we have for for questions guys but really great questions um definitely happy uh everyone um showed up and definitely um if a few more almost every week so we'll hopefully see everyone next time and then um yeah see you guys next time bye

2021-11-04 18:27

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