Godel has a Data Science Community, an initiative to unite Data Science enthusiasts and professionals within the company and an innovative space for exploration and growth. What started with an idea has now grown into a community of specialists who have the opportunity to come together and collaborate. We sat down with Katsiaryna Ruksha, Lead Data Scientist about how the community is shaping the future of Data Science at Godel and why Data Science isn’t a magic wand.

What inspired the division to create the community and when did it come about?

At Godel, we don’t have a dedicated Data Science division. As a result, people engaged in Data Science projects belong to different divisions. Before the Data Science Community, we had little visibility on what our colleagues worked on and had no points of connection. My idea back in 2022 was to find and bring together everyone who works on Data Science tasks or studies Data Science on their own and give us all an opportunity to collaborate.

What is a typical task to work on in the community? 

One of our main goals is to develop our skill set together. We take turns presenting to the group any topic that a presenter finds interesting. The topics vary from Math behind linear regression to quantum computing.

What’s the most interesting thing you’ve worked on using Data Science?

My passion in Data Science is music information retrieval and generation. Having graduated from a music school, I now research on my own how music theory and physics are connected to Math and used for modelling and generating new audio samples.

In what way is the community beneficial to Godel and our Partners?

Firstly, the community allows all the members to diversify their skill sets and learn new approaches. Secondly, we often have teams from other projects and divisions facing a challenge in AI or Data Science. Together we brainstorm solutions, share our experiences and help develop a plan of action. Thirdly, – and that’s what we are most proud of – due to the community, we’ve started to grow Data Scientists in our company. We already have a success story of a former QA Engineer – Dziyana Valenta – who worked under mentoring and moved to the Data Division to become a Data Scientist.

How do you stay updated with the latest trends in Data Science?

Reading relevant articles and sharing them in the group is one thing, but every year we also attend a Data Science Summit in Warsaw – a two-day event where presenters from different companies and institutions in Poland share their experiences about MLOps, NLP, and Computer Vision. That’s always a very interesting experience and an opportunity for us to meet each other in person and have some team-building activity.

How has being part of this community helped you in your day-to-day work?

That’s extremely valuable – to have people with whom you can discuss your challenges, sometimes complain and ask for a piece of advice. We start each meeting with a round of sharing our news and, if needed, a collective brainstorming on someone’s task.

Who would be an ideal member of the community? How can someone join if thier interested?

We will be happy to see anyone interested in Data Science, no matter what kind of division they are from or what level of DS skills they have. The only condition is to attend our meetings and be ready to present a topic in their turn.

What’s one myth about Data Science you’d like to bust?

People often think that Data Science (or AI) is a magic wand that can solve any problem. Blog posts that show how easy and quick it is to solve a toy problem even possessing no relevant skill set also add to this misconception. Unfortunately, this is not how it works in real production situations. Our work includes many carefully planned experiments, going back and forth to make sure that we provide stable and fair solutions with acceptable (and inevitable) error rates.