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Have you ever heard about a field called Data Science? In this blog post, we’ll uncover some of the secrets behind this very recent profession within the IT industry
Have you ever heard about a field called Data Science? In this blog post, we’ll uncover some of the secrets behind this very recent profession within the IT industry with the help of some of our Data Science team members. Let’s get started!
Data Science is the field that studies and analyses large volumes of data to improve the performance of a specific business. This information may be extracted and gathered from different sources through methods like machine learning.
Just like a standard scientist, this professional starts by looking for an answer to a specific problem. In Advertio’s case, the problems are always related to our product: our app. This problem identification is the beginning of the Data Science journey: the lever that starts the Data Science Lifecycle, a process with several steps that vary according to the project that is being developed.
Data Scientists are a must-have in a company like ours because they produce insights that impact directly the way our product works. How’s that? The more data you have scrutinised, the more information you possess to improve your customer satisfaction and the efficiency of your company!
Now that you already know what Data Science is about, let’s get to know how’s the day of our Data Science Lead, Diogo Sousa, and the perspectives of two other team members: Ying, who’s been in the Business Intelligence team for 1 year and a half and Filipe, the most recent member of this team, who arrived one month ago.
Diogo’s work day starts normally around 9 AM. For the rest of the team, it’s still very early — in Portugal, the country where the majority of the Data Science team lives, it’s 8 AM — so Diogo takes these first calmer two hours to develop tasks and/or to brainstorm about the next steps the team will take. After that, he usually has one or two calls in the morning with four different people: our CTO, Diogo Nesbitt; one on one calls with each team member — Ying, Elena, Shawn, Filipe, and Bruno -, product calls with the head of product, Matthew Gill or even the CEO, João Aroso. His morning closes with the Data Science stand-up, where everybody discusses what they’re up for the day. After the stand-up, Diogo goes to lunch.
In the afternoons, it’s also time for some calls, namely, sprint start meetings, mock-up reviews, user stories reviews, one-on-ones, team alignments, and planning. When there aren’t any, he’s usually helping someone with a task or answering requests from the Performance team with a bug or a ticket.
At the moment, since we’re actively recruiting for the Data Science team, usually one day per week Diogo also takes a morning or an afternoon to screen candidates.
The Data Science team is growing fast. In only one month the number of people duplicated: it went from three to six members!
The first member is, of course, the Data Science Lead, Diogo Sousa, who’s been working at Advertio for a little over two years and a half. Besides him, there are two recently founded subgroups: Business Intelligence (BI) and Artificial Intelligence (AI).
On the first team, there are two people: Ying and Elena. Ying has been with us for over one year and Elena just a little bit less. They are responsible for the part of providing reports, and all the information necessary for the performance team and the machine learning algorithms. They are also working on data consistency (so every team always has the correct data), on data analysis (trying to understand what’s happening and what we can do to improve), and on running some analytics and some models out of that data (so we can reach conclusions and improve our product).
The second group, the AI team, is composed of the three newest members — Sean, Filipe, and Bruno — who started more or less a month ago. They are going to work a lot in machine learning, so some of what they do is related to our AI Assistant Anna, on our app.
They are responsible for all the content suggestions, how to manage campaigns, the models to manage campaigns automatically, and work on budget estimations. This means they will work in machine learning and also on exploratory data analysis, understanding the data, and reading theory on how the networks work, so we can find better analytics, better processes, and techniques to improve our product.
At the moment, this is our Data Science Team but “hopefully we will get a couple more people to work a little more on data engineering goals and backend for data science”, Diogo concluded.
When it comes to the suggestions part on Advertio’s app, there’s a bit of software development (Backend) integrated so the team works with Python, Django, and Flask.
On the machine learning side, the team works with summarisation models, sentiment analysis models, entity analysis models, deep learning, and other machine learning models.
In addition, they also work with third parties to complement their in-house tech, for example, the translation service from Google and GPT-3. On the day-to-day, the team also uses Google Colab, an easy and good way to be able to write code and visualise it. For the reporting part, the team uses Google Data Studio.
According to Diogo, the Data Science team currently has two big and interesting challenges. The first project is related to the implementation of adpools, which are most commonly well-known as ad groups.
This step was a huge challenge: ad pools are responsible for refactoring completely the way the features are being suggested on the Advertio app and the Data Science team started this process almost all from scratch, which turned out to be very demanding.
Diogo also confessed that working with machine learning is very challenging because the models the team thinks might fit a specific problem, sometimes aren’t the most proper — which was what happened at first. In this case: “The first iteration didn’t have the best results so we had to think of what we could do better, what we could improve so the results were up to Advertio’s standard — which is a big standard because we always want to be the best.”
The second biggest challenge is creating a data warehouse. For those who aren’t acquainted with this field, Diogo explains: “a data warehouse is a place where you can store all your raw data and then have processes for extracting, processing, and loading data in different ways so you can provide that data to the different teams and the different mechanisms that we have inside Advertio.” This new tool will allow the team to have much better reports for the performance team and automatise the download of the data for any machine learning algorithms that the team is deploying or maintaining.
Then there’s a third challenge, more related to Diogo’s recent position as a Lead Data Scientist himself: adjusting to the transition from managing a small team of three/four people to managing a team of seven or eight people.
With more people on the team, the work has to be fully delegated to the rest of the team members. That doesn’t mean Diogo won’t be there to help if necessary — his job is also to help if his colleagues need something in the code, onboard them on new concepts that they still aren’t acquainted with in Advertio. However, to delegate is key and the trust he has in his team members allows him to take care of these tasks with the guarantee that they will be impeccably done.
When it comes to the other members of the team, Ying points out as a major challenge the management of tasks related to working remotely because the communication is more abstract: “For example, if you’re working in an office, and you have a doubt, you can just ask out loud and the person is right next to you.”. Filipe, the youngest member, focused on a current challenge: the evaluation of the content recommended by Advertio’s automatically generated ad suggestions.
Which characteristics does a data scientist need to have to work at Advertio?
There are some characteristics that Diogo, Ying, and Filipe point out as really important:
Also important to say is that although experience is important, it’s not an imperative factor. According to Diogo: “We never search for people who have 10 years of experience or 20. We are a start-up, we want to grow with our employees and grow all together.”
First, Diogo points out the amazing environment. He told us everyone treats him really well — not only his colleagues but even the executive team. Filipe also subscribed to this opinion: the ease with which you can talk to most of the C-suite executives in Advertio is not very common in companies.
Secondly, the support from the company. When Diogo decided to move from Portugal to France, the company tried to find the best solution for him to continue in the team.
As a third topic, he points out that the company allows him to grow — at Advertio it is allowed to fail, as long as you learn from your mistake and keep striving for excellence.
“They let me grow, let me learn, let me make my mistakes and learn from them, which is awesome.”
In this sense, the data science lead also told us when he arrived at Advertio, a small start-up at the time, he was far from thinking that in two years and a half, he would be leading a team of seven people.
In fourth place, is the impact. Filipe arrived only a month ago but highlights that in Advertio he found out exactly what he was looking for: a place where he could make a difference and an immediate impact with his work.
Last but not least, just like Bernardo and João, the company culture is a big part of the satisfaction of working at Advertio. The Data Science team recognises that the unlimited days off (yes, this is really a reality!), the flexibility of the schedule and the possibility to work from anywhere that allows them to balance their personal work-life are decisive factors in their motivation to continue working with us.
Are you a Data Scientist and would you like a new challenge in your career? Then Advertio is looking for you! Join our team here.
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