Data Science at Wildlife: A deep dive into how our team works
Data is paramount to Wildlife’s business, from understanding user behavior to creating innovation in our Ads practices.
Written in collaboration with Carlos Sarraute.
We at Wildlife understand that even though our business is, quite literally, “all fun and games”, the “business” part is still a key aspect of our lives here. As you’re probably aware by now, our company is data-driven. We collect and interpret all kinds of information to develop new, innovative ways to make our products lucrative, while at the same time keeping them free and open to consumers and monetizing them through digital advertising.
That’s where we come in.
You see, when we say we’re Wildlife’s “Data Scientists”, people tend to mix up some things: even among those who have some understanding of data work, it is not rare for them to think of us as “Data Engineers” — the difference between both things actually is much clearer if you’re part of our daily routine.
But to avoid getting too technical about it, we can say that, in a nutshell: “Data Engineers” are awesome at directing collected and verified data to where it needs to go — our data lake. But after that, we “Scientists” collect the raw material from a data lake and analyze what it is and what it means. It’s a bit of a two-way work, where engineers and scientists depend on each other, but even though we are close, we’re not quite the same.
In a sense, we find meaning to the data, establishing patterns and values that other teams (every team!) can benefit from. And that difference is important here: on average, we receive 18 TB per day of info (as in “terabytes”) of information from players all over the world.
From that raw data, we work to generate conclusions about the players: not only their behavior but also what drives them to download our games, how much they talk about them with their friends and acquaintances, and every other bit of data that tells us how good our games are to them.
Handling raw data and getting insights
Carlos Sarraute — Sr. Data Scientist Manager and co-author of this article — and I, work specifically on the Ads Monetization team, which is responsible for analyzing all that raw data and creating innovative ways to promote our games through social channels and other platforms that allow for advertising features.
And to handle all that, we need a sizable structure to house all of this data, which is why we use cloud-based solutions from Amazon Web Services for hosting, but that’s not all.
We divide our projects/analysis/modeling work into clusters for each data science team to work with it. A “cluster” is “a set of computation resources and configurations on which you run data engineering, data science, and data analytics workloads".
We have the marketing cluster, game development cluster, legal cluster… that are used by data scientists to offer insights to other teams. Every data science squad has its own space from which to access curated partial data so they can improve, in a more certain manner, their business practices.
A practical application: what we do
Let’s give an example of how we operate: let’s say that a certain number of Brazilian players are skipping our ads more often. We will never know that you, specifically, did not wait the mandatory 15 or 30 seconds from the ad piece’s running time before skipping it, but we know that a user did it, and what really matters to us is that a group of people that have similar details to this user, normally behave this way. So we can look at the entire package of data and determine why a handful of people are doing that, and plan our actions accordingly.
In this particular example, we can surmise that that particular ad piece has been running for a long time, so we know it’s about time to create a new, more fun piece — one that can lead you to download our games or play it more often due to a holiday campaign or something of the sort. That interpretation is then sent from us to the design and advertising teams, along with recommendations driven from our models, so they can come up with something new, which we’ll then use to run new ads in our back channels.
Collaboration with other teams
That interaction with the other teams is also paramount to us: one level of it is the work we execute with other data science teams. We share common knowledge and expertise between every squad, and then every squad works that information on its own vertical — Carlos and I, for instance, are in Ads Monetization, so we are the team that generates the variables that we use in our advertising, choosing which ad to display where.
With the information of those variables, the models we train with, and the conclusions that they generate, we work closely with the people that work on events inside our games, such as holiday season special items and such.
And there are also data science teams dedicated to benefit the player. The fact that we have ads allows us to distribute our games for free, so in a lot of our products, seeing ads is something beneficial — you choose to see an ad, you get an in-game benefit.
There is something that maybe not everyone thinks about, as well, and that’s the fact that whatever benefits the gamer, also benefits us: the gaming experience, for instance, is something that tells us when to show an ad for a game, to not spam the user with banners.
Different backgrounds, same mission
Currently, we have 57 data scientists worldwide — 11 of them here in Buenos Aires. One of the things that I love about our team is that we are eclectic about our skills. I started in Industrial Engineering and Carlos is a mathematician. In the squad, we also have a Ph.D. in statistics and one in physics… So we have a lot of diverse backgrounds, which helps us as a team, because we cover a lot of ground and knowledge, hence being able to tackle new problems with different mindsets.
Sure, we ask that candidates know the basics of data science and machine learning. We have a very thorough interview process where we evaluate all of that. But we also take into consideration the curiosity and research skills that you may bring to solve problems on the go. We’re building a world-class team.
Besides the background that we come from, I think that what is very clear in our team is that we have broad previous experience in the field. It becomes clear that you’re talking with people who know how to work with data.
So, as we said at the start of this article: data is essential to us. We have a lot of fun working with it, and it allows us to, in a specific manner, know more about you — and Wildlife always thrives on understanding its users a little bit more every day. We’re doers, and we’re glad to be like that all the time.
This article was inspired by a talk we presented in Nerdearla — the biggest IT event in Argentina — 2020 edition. If you want to dive a little deeper into our work, check out the video! (Autotranslations available)