Celebrating Women in STEM: An Interview with CentralNic's Kasia Wrona

A significant gender gap has persisted throughout the years at all levels of Science, Technology, Engineering and Mathematics (STEM) disciplines all over the world.

Though tremendous progress has been made, and many voices are amplifying women in STEM, they are still underrepresented in these fields. 

To commemorate today's International Women's Day, we wanted to showcase the work of one of our own women in tech, Kasia, who is a part of our Data & AI team. So we sat her down for a little Q&A…

How long have you worked as a data scientist?

I’ve been working in data science for four years now – one of those at CentralNic.

What route did you take to arrive at this career? Did you study data science or come to it from another field?

I started my career as a Financial Analyst, where I developed my interest in the field of data. Faculty connected with data science was the third area I studied (after finance and accountancy and railway logistics) so I think I kind of came from another field and transitioned, or just filled in some missing skills during the very beginning of my career. I was definitely more interested in databases than in financial statements!

What's important about data science for CentralNic?

CentralNic has doubled down on becoming a truly ‘data-driven’ company. In the context of a Group like ours, this means making access to data and the results of analytical activities available to different parts of the company. Personally, I went really deep into understanding our customers' needs and behaviours based on the data crumbs they leave behind.  

Data is an important asset and we’re becoming better at extracting useful information from it. Also, Big Data analytics allows us to see more, e.g. monitor how a customer uses their domain and recommend different additional services based on user activity. Or, we can track user activity on the website and feed it to marketing automation tools. For example, if they visited a website three times and made no purchase, maybe they need some guidance.

What I find particularly interesting, and I think sets apart groups of companies from single entities, are the many challenges related to collecting data points from various parts of the company due to different systems, permissions, etc. Our Data Engineering department is doing an excellent job getting the data together, but the challenges of data cleaning and understanding context constantly push my limits. I learn so much every day and I find this to be a really evolving aspect of my work.  

Why do you love working in the field?

I love data science for being so logical - I often have the impression I'm stacking blocks or solving puzzles. It's really satisfying when I see the results of my work help make better decisions and move the company in the right direction. Sometimes these are just small elements, but when brought together they make a difference.

What's important about data science for the world?

I think nowadays there’s a discussion in the public space around algorithms - whether it’s safe to rely on them and if/how they should be accountable for their results. There are examples in history of biased algorithms that discriminated against groups of people, for instance.

I guess we as a society have to answer questions like this from an ethics and law perspective. It’s worth remembering that an algorithm is a tool like any other - maybe just a more complex one. That’s why it’s so important to create algorithms that are verified, trustworthy and not biased. Like with any other tool, algorithms that are not used carefully or as intended can be harmful.

…and you’re a mentor as well?

That's true, I'm a Mentor for ‘Career Foundry Data Immersion’, an online course for people who want to change their careers and transition into the data analytics area.

I support my students in creating professional portfolios. I also give them hints on how to look for a job. It's a very satisfying role and it gives me the opportunity to share basically everything I've learnt so far in data analytics. I explored this path on my own, so I’m glad I can help others save time and energy with my insights. Students of this course come from different parts of the world and have different experience levels (from graduates to C-level management), so it's also an interesting perspective to gain.

Thanks for talking to us, Kasia, and keep up the amazing work!