When I was there, I had a chance to interview Joma Tech on just in case you don't know much about him.

Hey, went to the University of Water in Canada for a computer science degree while he was there.

Hey, did a bunch of internships in software engineering that science on P M companies like lengthening Facebook on Microsoft on After graduating, he worked as a data scientist at Buzzfeed on Facebook on.

Now he's a youtuber to now in this part of the interview that I'm going to show you now.

We basically talked about first of all, what it's like to be a data scientist and also how to become one s.

So let's just jump into it on.

The first question I asked them was, What was he like to work as a bit of scientist at both feet and Facebook?

The soul?

Um, I could talk more about Buster.

You may be a bit less what?

Facebook.

But aside, buzzfeed, I was basically hired to understand, like how to do how to distribute videos on Facebook.

So how it works is like, who has you know Buzzfeed is like a media company.

They make a lot of videos.

They make a lot of it was everywhere, and one of the platforms that they care a lot about is Facebook.

So if you've ever seen like, a tasty video of the hand and stuff, that's that's buzzfeed.

So basically, my job was toe.

Try to understand our Facebook strategy.

You know, they have a lot of questions, like, should we share or Sherry Cross posts, which is very like specific to like, Facebook and stuff like that.

And then also, like, what kind of videos do better than others and stuff like that.

So basically, my job is, um, is toe basically like a product analyst for Facebook videos?

And then a lot of the times.

What we would do is who would just look at a lot of these videos and try to understand like, Oh, why certain things happen?

Why did we get more shares unusual and then, yeah, that's pretty much it.

And then at Facebook at Facebook, I worked on like the video team, and basically what we're trying to do is we're just trying to make videos more successful on Facebook.

Yeah.

So it sounds like your career, like your entire career.

All about videos.

Yeah, pretty much.

So, Uh, that's that's, like pretty much my choosing.

It's because even before starting at buzzfeed, I was always into, like, videos on dumb.

It's not, like, very good, but I was pretty like, I really want to be a YouTube celebrity for some reason, you know, as a very vain of myself.

But yeah, even chose, like, my career based on that.

And, you know, like I chose buzzfeed because I thought that I might be able to be in their videos in l.

A.

And then maybe I'd get some recognition and then using that recognition, I could grow my YouTube channel.

That didn't happen, by the way.

So, yeah, I actually went to Buzzfeed for that.

Yeah, And then when I went to Facebook, my initial thought was not to be on the video team because I was like, Okay, I'm done with videos because when I switched to Facebook, I didn't want to do anything with videos anymore, But it was just so tempting because the team was like, Oh, the creator team.

I was like, Oh my God, It was like it sounds.

It was almost like, not destiny.

But it was like my calling.

So I was like, Okay, fine.

I'll go on that team because on Facebook, you, um you're in boot camp first, and then you get to choose what team you want to pick later.

So I didn't initially want to be in videos, but India and I still ended up there.

Yeah.

So you said that you wouldn't be able to share it that much stuff about the work you did at me because some of the stuff is still like, like, not public or also, like, they don't want to tell us like this strategy toe, especially YouTube and stuff like that.

But, um, yeah, I could talk about it vaguely, though.

Okay, let's focus on buzzfeed.

Okay, s So are there any interesting examples of data science work?

Yeah, um, I'm gonna be quite on us here.

Sometimes, I think like not every company needs a data science team.

And buzzfeed was kind of on defense of it, especially from what I was working on.

Because, like, it's kind of like like running a YouTube channel were like running a Facebook channel.

Essentially, I'm an analyst for their Facebook Channel.

And a lot of the times you can't really explain stuff with data with stats, cig, you know, like one of the questions was like, Oh, you know, should we share should be cross pulse.

Which one's better, which will get more views.

But the issue is you have to, you know, work with the people who actually managed to page make sure they post at the same time that same everything.

So basically, we did a lot of, like, crappy A B tests like it wasn't a clean, maybe test to try to discover if one thing is better than the other.

And we were cared about like a sharing better than cross posting stuff like that.

And then another kind of work.

We did a bus.

Feet was just understand the health of our Facebook pages and one of the things like it.

It doesn't even require that much data, science skills or anything.

But we just looked at, you know, how many views are we getting like?

We saw that our page like tasty.

They were getting less and less views per video, so meaning like we were making a lot more videos just to get the same amount of you as we did before.

So then we have to tell that the content team saying like Oh, by the way, this might not be working.

We need to think of different strategies to get one of you's.

So it was very similar to like what I do for my own YouTube channel.

We're just like looking at Bear's videos and various like trends and then try to capitalize on the No, no, no.

So that doesn't sound too technical.

Yeah, it's not that technical.

I mean, to be honest, like a buzzfeed.

I mean, there were some other things that are more technicals, like like I automated.

They're like dashboards, automated, like e mails and stuff like that, because they were pretty behind in terms of like automation and infrastructure.

So sometimes what I would have to do is like, um, I would have to like, right slap slack bots.

For whenever something happens, I have to tell like this, like what has to tell you the content himself like that.

So I would guess that's technical because, you know, you have to call it a little bit, but, you know, like in terms of technical nous, it's not that technical, but even at Facebook, it wasn't that technical.

Okay?

All I did was, like, sequel sequel queries and then use, like, tableau, toe like visualization.

And then, if I need it, like to do, like a simple inner aggression, or like, a logistic regression that I would use, like our python.

But that's it, right?

Yeah.

Is there anything more you might be able to share from Facebook?

Um, thank you.

Well, I guess we're Facebook.

Like I could say that we have a lot of data and way, have a lot of data, and it makes things a lot easier because, like, we have a lot of data engineer.

So, like, things are really, really easy to do.

All I need to do is, like, right, sequel, query.

And I could quickly, like, billions of data points just in one query and then, yeah, that's very much.

And like the job that I did at Facebook, we basically wanted to make creator successful.

So, you know, you could you could kind of, like, assume you could kind of like, um in for what I did.

But basically, we look at our platform Facebook as a whole, and then try to understand how to change the platform.

Such debt can make creators counting your creator successful in our platform.

Oh, yeah.

So I guess ah, one like hypothetical example.

Might be life video versus regular video.

Yeah, that could work, too.

It's like we could try toe, like, compare two different types of videos and understand which is better.

And then if we're sure that one is better, we could tell, Like our partners to say, Oh, by the way, you should do this more.

It will increase your distribution.

So, yeah, we would get a lot of understanding of our own distribution ecosystem and then tell our partners like what to do?

It's a partner's video creators.

Yeah, like video creators.

Like, for example.

You know, like, um, like, Buzzfeed's a partner.

Basically anyone who was big on Facebook, you could kind of guests at their partners, meaning that we have at least like a few people talking them all the time.

Okay.

All right.

Uh, another question I have for you was how did you get into the defiance?

Yeah, So I think so.

So I did do computer science as my major, and then I want to be a self for engineer.

I'd even think about day of science.

And then at one point, I remember I was at Lincoln.

We were like nine interns, all from Waterloo.

We all live together.

And then at one point, I remember one guy want one friend.

He said, like, software engineering Isn't that great?

I was like, we would he means, like, I think I would rather like if I got paid the same.

I think I would rather wash dishes, then be a sovereign Jesus was like, Oh, my God really isn't that bad, you know?

But then that kind of Blake that kind of stuck in, you know, and and the more we talk about it, the more understood what he was talking about.

So he was talking more like as a software engineer.

He feels like he doesn't have it.

He doesn't have as much say in the product direction as he wants so coincidently.

A lot of people who interned at Lincoln with me, they all wanted to be like product managers are entrepreneurs yourself like that.

And then I guess I also want to be a like a startup guy back then and what's closest to like leadership because I thought it was cooler.

It's like old leaders and stuff like that.

Usually, PM's are like leaders, and they usually end up getting into more senior like Executive Rose.

So then I wanted to do him.

And then when I applied for jobs, um, I realized that data science might be a little bit closer to P.

M.

And then that's how I started on beta science because And also, I just asked my friend on my okay, refer me toe data science position and my friend referred me.

And then that's how I got my first internship.

And then that's how I discovered.

Like, Oh, you know, data science is not about like a machine like it's not always about like machine learning, like deep learning stuff like that.

A lot of it is like a business intelligence job.

So yeah, that's how I got into it.

So you were a software engineer out Langton, and then you got interested in P.

M.

And did a science and you started applying for those jobs.

Did you do anything to prepare for their science interviews.

Yeah, uh, I learned sequel because I never did sequel.

And I also learned probability questions.

No base there.

Um and, uh, yeah, I kind of brushed up on stats like, I did learn some stats in school, but, you know, I don't remember everything, so I have to, like, just read up on it.

And then, obviously, I went on the glass door, checked, like what kind of questions they asked and then, yeah.

So that's how I prepare for the interview.

Like I knew that it would be easier in terms of, like, coding Or like a technical white boarding interview.

I knew that would be easier.

And I was right.

So I didn't need, like, that much preparation is already prepared law for, like, soft recording jobs.

Yeah.

So going through those topics was actually helpful for those interviews.

Oh, yeah, for sure, because I literally did not know sequel.

Okay, So if someone wanted to get into data science today, how would you recommend that they prepare?

Yeah, so kind of like it really depends.

What do you mean by their science?

But if you mean like my data signs, like Data Analytics.

Then I think we should do.

Are you and Sulphur?

Are we pretending that this person is in software?

Wants to switch to, uh, think this person could be either in software.

Okay.

Something out.

God, I got it.

Okay, so I guess, um, yeah, using my exam, my previous examples of, like, you know, Buster and Facebook, I guess, for Buzz I should know both them is quite similar to interview process.

So I think what you need to do is first, um, yeah, I learned sequel.

If you don't know that already, it should be pretty simple.

Um, you should just, like, look it up on the Internet, do a few secret questions, and you're good.

And then you should learn to code a little bit like python, for example, because they will ask you, like, maybe one or two.

Easy python question.

They're not as hard as, like, software engineering ones.

And then make sure you understand the basics of probability and stats.

Stats mean like, you know, confidence Interval is, like, very basic.

You know what a normal distribution is?

Do you know howto do like, yeah.

Just knowing how to do confidence intervals and then knowing you're like distributions like binomial distribution Russell and stuff like that.

Because sometimes they might ask that.

And then that's it.

Yeah, like that would be pretty good enough for entry level.

Oh, yeah.

One more thing.

But I didn't study for that.

But you have to have, like, product sense.