The Harsh Reality of Being a Data Scientist

hi friends welcome back to another video in this video i wanted to specifically dedicate it to the reality of working as a data scientist the harsh reality actually i know there's a lot of glamorization of the data science job there's a lot of content talking about how to get into data science and i think it's only fair that we also talk about why is it difficult working as a data scientist in the industry and i'm not trying to be negative i think i'm just trying to highlight and shed light on things that i have personally

experienced um and have seen and that's what i'm doing i've been in the industry for about nine years now like hard to believe i feel like i just graduated yesterday i've been in the industry for uh yeah about nine years i've been through like multiple data letters i started out as a data engineer then transitioned to data scientist role so i think i've learned my fair share of things and i've seen my fair share of things to be able to like talk about that so without wasting any more time let's get into the video so

whatever i share in this video is based on my personal experience it is possible that this does not reflect your experience so no need to get offended in the comments but i just wanted to put a disclaimer and again like i have put it in my bio like all these opinions are my own and does not reflect of any of the companies that i worked at or currently working at all right so without wasting any more time let's jump into it i have seen a lot of people transitioning out of the data science ladder sometimes

it like takes them years to get into data science and as soon as they get in like they work for a few years and then they leave and the most common job family that they end up going into is product management which is i don't know if this is like correlation or causation like does data being a data scientist prepares you to be a better product manager like or is it because like you have to have a better communication so you're like qualified and you're involved with the product i don't know like what is it

what is it that why is it that a lot of data scientists end up who end up transitioning out of the job family end up becoming product managers like i'm genuinely curious why they do that or why is that the case i have been offered so many times to become a product manager one of my last managers was giving me feedback and some of the feedback was negative and he actually mentioned to me like i should consider like becoming a product manager and one of the things that i bring to the table um is that

i have great communication skills like this channel exists because of my communication skills and as soon as you have good communication skills i think like you can be a great product manager and i've been pitched not once but actually multiple times to transition into product management which i do not want like i started being so vocal whenever i go to my mentors now i and i'm asking them asking to get their advice in terms of what i do next i actually have to put a disclaimer in front before we actually start the discussion but and

i share like what i'm thinking of like i i tell them like the product management is something that doesn't interest me and i like that's not an option so if you're going to give me advice like make sure that does not include that because yeah am i the only one who has to deal with this like for those of you who are working in the industry is this something that you have to deal with but anyways so a little bit of ramble on that a lot of people end up leaving the data science java family

and i think there's various reasons for that one is like it's very hard to grow within the data science ladder the job family is ambiguous like people are still trying to understand like the companies are still trying to understand what to do with the data scientist job family a lot of people don't even understand what your role is some people treat it as like a data analyst role some people treat it as like a software engineer some people just treat it as like whatever like you i personally have to like educate a lot of people

on on the job family itself and i started working with somebody on a new project so yeah i'm going back to my point like career career growth is hard which makes it like super ambiguous if people around you don't understand how to evangelize you best and especially if you're new in the industry and your manager or the people you work with do not understand your role you're gonna get put on a project that you actually is not in your scope or not part of your job so it's how is it gonna look on your performance

and you're when it's being when you're being evaluated for a promotion because the expectation does not match the work that you have done um and the other thing about people leaving and transitioning out of the data science job family is that it's discouraging it's demotivating because when you see a lot of people leaving it makes you question like if you should be staying it is definitely not inspiring at all when people leave the data science job family i have had in my organization i had a lot of people leave the data scientist ladder and i

did question the leaders as to why people are leaving why is why are they not growing within the letter but i haven't solved that case so let me know if you have any thoughts there um the second thing that makes a data scientist job family a hard one is the interview prep yes the interview the second thing that makes it like really really hard is the job search and the interview prep i have talked about in my previous videos a lot because i had suffered from it it's so intense preparing for a data scientist job

searching a data scientist role and then interviewing for it the for the job search you have to like figure out because the data scientists at one company does not mean a data scientist the same data scientist at another company so you have to do a lot of research and figure out like what exactly is your role and then what exactly is the role that you're applying to so the titles don't match which means you have to do a lot of research do that read the job description which increases your workload and even then sometimes when

you you'll go into interviews that turned out to be like super opposite of what the job requirement said uh for example i've been an interview with few companies where basically the the interview was like written for a data analyst um the whole interview was about like sql questions which made me wonder if this is actually even though the title says data scientists this is actually is this actually a data scientist role because all the questions that they're asking me is for a data analyst and i'm pretty sure the rest of the people who were working

there they interviewed for the same guild and then got hired so that made me question the whole thing so my interview process was horrible like horrible i did not enjoy it and i do not look forward to ever interviewing again which i know i will and i have to like mentally ready to do that but the whole thing is like just so messed up because you will spend a lot of time applying to jobs first figuring out what to apply for then you would go to the interviews and then at the interview after doing like

five loops you'll like realize like this is actually completely wrong i i'm this is not a data scientist role this is something else and like you move on and then you wasted basically your whole prep and your your like your loop so it's it's kind of like a kind of like a it takes extra work to find the right role that defines the data scientists job family the way it should be or the way you think it should be so one of those so that's was my second thing the third thing which i think is

possibly just specific to my it might be just my experience and i i'm not sure if anybody else has experienced it at my last company i was working as a data scientist and i had this great manager who i really enjoyed working with him he really understood the data science jaw family um and we worked very well together he ended up leaving and i got a new manager and that manager basically used to manage a lot of software engineers and now he's managing data scientists as well one of the things that we would constantly hear

in the meetings when there is like the team is trying to hire a new data scientist um there is also another role called applied scientists and i've talked about in my videos somewhere here you can look his comments were always like we're gonna hire an applied scientist we're not gonna hire a data scientist and i have asked i've asked questions like why you're not opening this role for a data scientist because like this the description that you have the work for the work like it totally qualifies for a data scientist why are you opening for

applied scientists and his comments would always be and i've heard this multiple times from him his comments would always be that applied scientists are more fungible by fungible i had to look up that word fungible so fly scientists are more fungible fungible tangible basically an applied scientist is a mix of a data scientist and a software engineer so if you hire applied scientists you're actually getting a two in one so you can like give them the software engineering work you can also give them the data science work so why would you hire a data scientist

when you can hire an applied scientist who can actually swing between the two they just sound so funny but i just like i felt like i just i i felt like whenever he would say that like i would feel so offended because like every role has its own reason to exist right like if if applied scientists was the role that it rolled and why would there would be a data scientist job family so every role has their own purpose and that's why a company is created so i just was super i was offended obviously i

never liked i never liked whenever he brought it up and i didn't feel the need to argue maybe i should have maybe i was too junior and i should have done that but the constant the constant pressure to be compared by other tech technical job families uh such as software engineering or applied scientists was just not healthy for my but it was just toxic it was not good for my own mental health whenever those comments were passed i was felt i was made feel like i'm like less important than other job families which i don't

know if maybe it's true don't like tell me that the the role anyways so um and the problem with him was that he didn't fully understand what it what how to utilize a data scientist fully so he kind of like went for an applied scientist because now it's fungible i don't know why i keep saying that word but like that's a problem in the industry like a lot of people won't understand um what you're supposed to do which puts you like awkward spot and especially if your manager doesn't understand your career development growth is going

to be highly impacted by their understanding of what a data scientist is supposed to do so something to keep in mind but i do know like the industry is getting better and there's like more education um i one of the tips that i would give here is that when you're interviewing make sure that your manager has a very good understanding of data science job family ideally if they were previously working as a data scientist as an ic and then transition to manager like those are like great managers to have because they have actually done the

work to fully understand what's in scope what's not scope and how to grow you in in this ladder i've always been mindful when i'm interviewing like it the manager that i'm interviewing is coming from a non-data science background and the fourth i don't know if this qualifies as a harsh reality or more for like a personal experience i feel like like most of the things i've shared have been personal experience but uh the imposter syndrome uh you're working in the sexiest job of 21st century according to harvard business review there's a lot of expectations that

you have from the star family there's a lot of hard work that you put in to this job family so once you make it you made it right but then comes imposter syndrome where you are basically feeling like do you even deserve to be here like are you fraud like do you even qualify for the work that you're doing and on the second hand you're like actually doing the work and you're like oh my gosh i'm just like cleaning data what am i doing majority of the times i'm just cleaning data the expectations and realities

do not match and then on top of it you have this imposter syndrome that is like kind of like holding you back um so it's like a mix of emotions and mix of things that are happening i think like i personally been through my fair share of imposter syndrome and i'm like actually over it so i've like talked about it in some of my previous videos you can watch those videos but posture syndrome is there you might experience it just know if you experience it you're not alone and it's not specific to the data science

draw family but i do want to mention it because it does happen especially if you're working in a very glamorous job family such as data science data scientist role which we hear a lot about in youtube on youtube on news and like um the harvard business review article i don't know i think i covered most of the things that i want to talk about do you think i missed anything what are some of the things that you wish that were more obvious to you before you got into the industry and start working as a data

scientist like let me know in the comments and the things that i share today do any of these resonate with you or am i just like having a bad experience or had a bad experience i don't know let me know in the comments and anyways i hope you're having a great day and i will talk to you in another video have a good one