Sometimes we forget the humans behind the tech in our ever busy world. DSF is fortunate enough to know some incredible tech leaders across the world and has the privilege of hearing them present at our events. That being said, our Speaker Spotlight sets the stage to get to know our speakers on a more personal level and connect them with our growing community. Read the mini interview below!
A bit about Georgios:
Georgios Giasemidis is a Senior Director of Data Science at Choreograph, WPP Media, and a basketball analytics practitioner at the AUEB. He holds a PhD in Theoretical Physics from the University of Oxford and brings over a decade of experience in data science, machine learning, simulations (agent based modelling) and AI, applied across diverse industries. Georgios combines his expertise in statistics and numerical analysis with his passion for basketball, and he is the author and maintainer of the ‘euroleague-api’ Python library. He is also deeply committed to mentoring the next generation of AI scientists, actively participating on the MentorCruise.com platform, where he maintains a 5-star rating and scores in the top 8% of mentors.
How did you start out in your tech career?
I began my career in 2013, shortly after completing my PhD in Theoretical Physics. Initially, my goal was to stay in academia and pursue a postdoctoral position. However, due to limited funding opportunities at the time, I had to pivot. So, I decided to continue into mathematical modelling of real world problems and work on data analytics, the term data science was not popular yet. This is a field full of intellectually stimulating problems, where I could apply my analytical thinking, mathematical background and problem solving skills to real-world challenges.
What are the signs of success in your field?
In data science, success isn’t just about delivering models or dashboards, it’s about sustained curiosity and growth. For me, the key signs of success are working on problems that genuinely excite you, continuously learning new methodologies and technologies, and being part of a team where knowledge flows both ways. Being in an environment where you can learn from others and also help others grow creates a strong foundation for long-term success in the field.
What is the best and worst thing about your job role?
One of the aspects I enjoy most in my role is mentoring less experienced data scientists in the team. Mentorship is very much a two-way street, not only do I get to share knowledge and help others grow, but I also learn a great deal from my mentees through their perspectives and questions.
As for the part I enjoy the least, it would be working with messy datasets. While data cleaning and preprocessing are essential steps in any project, they can be time-consuming and less intellectually rewarding. That said, the landscape has improved significantly in recent years, with more advanced tools and libraries helping to automate and streamline much of that work.
What can you advise someone just starting out to be successful?
Be patient; breaking into tech today is more competitive than it was five years ago. Focus on building a strong portfolio, especially around projects that genuinely interest you. Whether it’s sports, music, or social impact, tying your work to your passions makes learning more enjoyable and your projects more compelling. Stay curious and committed to continuous learning, keep up with new methodologies, tools, and trends. And remember, growth takes time, consistency and curiosity are key to long-term success.
How do you switch off?
I have a few hobbies that help me switch off. The main one is basketball, mostly watching these days rather than playing. Interestingly, what started as a personal interest evolved into something more technical: I’ve developed Python packages for basketball data collection and analysis, so even in my downtime, I often find myself writing code to better understand the game.
When I want to disconnect more fully, I really enjoy building Lego sets—it’s surprisingly therapeutic and a great way to focus the mind. I’m also a big fan of comedy, particularly stand-up. And when I want a complete escape and disconnect from screens and the world, I turn to scuba diving. It’s the perfect way to unplug, quiet, immersive, and completely separate from my day-to-day work.
What advice would you give your younger self?
Don’t rush through milestones, progress doesn’t have to be linear, and putting pressure on yourself to hit goals too quickly only creates unnecessary stress. Take more risks early on; that’s where real growth happens.
What is next for you?
Career-wise, my focus is on growing my team and increasing its impact within the organization by working with cutting-edge technologies and building outstanding products. Personally, I’m excited to further develop my basketball analytics side projects and explore opportunities to collaborate with basketball professionals, aiming to turn this passion into a sustainable venture.
If you could do anything now, what would you do? Why?
I’d love to take my basketball analytics projects into collaboration with professional European basketball teams. There’s a huge opportunity for teams to gain competitive insights from advanced data analysis, but it’s still an underused resource in European basketball. Bridging that gap between data and performance would be incredibly rewarding.
What are your top 5 predictions in tech for the next 5 years?
We’re currently in an era of rapid AI advancement—methodologies that emerged just a year or two ago can quickly become outdated. Predicting the future is challenging in such a fast-evolving landscape. Many believe that large language models (LLMs) and AI agents will soon automate much of a data scientist’s work. I agree that once these tools reach maturity, they will dramatically accelerate how we work. Here are my top five predictions for tech in the next five years:
- The maturity and widespread adoption of AI-powered coding and data science agents.
- The democratization of data analytics, making advanced tools accessible to a broader audience.
- The emergence of new AI architectures or methodologies that push beyond the current performance limits of LLMs.
- Hot take 1: Quantum computing
- Hot take 2: Breakthroughs in superconductors
Watch Georgios’ session at the Data Science Festival here.
Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!
Want to become a DSF Speaker? Apply here!