NLP, Friday rituals and connecting the dots: Matyas Juhasz
Who is Matyas? Tell me a bit about yourself…
I’m a Data Scientist at Climate Policy Radar. I’m a mathematician by training, and am interested in how we can use maths and statistical models to better understand the world around us: unveiling connections, or hidden patterns. That’s what drew me to Natural Language Processing (the ability of a computer program to understand human language as it’s spoken and written, abbreviated to NLP) which has allowed us to ‘connect some dots’ that we hadn’t previously been able to.
Can you tell me a bit about what led you to Climate Policy Radar?
This answer has two parts, one on climate and one on policy. Let’s start with policy. After university, I worked in tech consulting, which is where I first started working with NLP and AI. I have always been interested in society, and how humans live and organise themselves, which is what drew me to policy. Regarding climate, I was looking for a role that was fully aligned to my values, in the tech for good space. I think most of us can agree that the biggest unsolved challenge we currently face is climate change, and I was interested in the application of NLP in this domain.
One thing that I’ve found so rewarding is finding a growing group of people working on these thorny problems within the Climate NLP community (a community of practitioners who work in the domain of NLP for climate change that Climate Policy Radar convenes weekly). They are often open source, not-for-profit like us. That creates a great atmosphere of alignment that I find really motivating.
What excites you about Climate Policy Radar?
The culture at Climate Policy Radar is unlike any place I’ve worked before. I’ve found a level of empathy and comradery that I hadn’t experienced elsewhere. We’re definitely all in the same boat, with the same mission. There is a strong emphasis on collaboration and making things work in teams and across teams. It’s an environment where you can thrive, be proactive, and do great things.
What does a day in the life of a data scientist look like?
Days can be really different depending on the project I’m working on. In general, I’ll spend a good portion of time creating and understanding datasets or training machine learning (ML) models, and everything that involves: writing code, evaluating results, going through our datasets. A big part of our role as data scientists in the climate community is what I’d call ‘translation’, from policy language to our language and vice versa. Our team is cross-functional, we work alongside policy experts, so there is constant learning. It fosters creativity on both sides.
Where can you be found on a Friday?
The longer weekends are great, I’m able to do things that I previously didn’t have time to do: one of my Friday rituals is that I sit in my local coffee shop and read a book for a few hours in the morning. I’ve found that to be really fulfilling, because it’s not something you often make time for. Other than that I go for walks, run errands, and go to my climbing gym, avoiding the weekend crowds!
