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 Hannes:
With masters in both machine learning and applied mathematics from KTH Royal Institute of Technology in Stockholm, Hannes now works as a full stack data scientist at the fast-growing fintech company, 9fin, in London. He is product-minded, with a “ship early” mentality, and has an extensive background in NLP, LLMs, information retrieval and data engineering. He has experience with both research, model development and implementation using the latest cloud services.
How did you start out in your tech career?
At Christmas, during the second year of university, my mum gave me a book named “Life 3.0: Being Human in the Age of Artificial Intelligence”. It had just been published by Max Tegmark, a Swedish AI researcher and professor in physics at MIT. I typically struggle to find books that catch my interest but this was something else. By combining his deep technical expertise with a philosophical view, Tegmark made me realise the power of AI and how important it is that we get it right. I instantly knew this was something I wanted to work with.
Soon after, a university friend who had adopted the same interests invited me to join their team in the world-wide NLP research competition. It was called the ‘Amazon Alexa Prize’, and the objective was to make Amazon’s smart speaker, Alexa, more social. We actually reached the top five. Without much programming experience, I joined the PhD students as the Head of Marketing, although I was mostly interested in discussing and learning more about the technical approach. Since then, I’ve done everything I can to educate myself and progress in the field.
What are the signs of success in your field?
I believe that a successful data scientist is well connected to the business side and has a track record of delivering user value rather than trying to apply the most advanced models. Interpretable and simple solutions are so much easier to communicate to stakeholders, debug, improve on and ship early.
I also think it’s essential to have skills ranging from both data engineering, software engineering and domain knowledge, in order to deliver maintainable solutions and get stuff done. Communication is also key and another sign of success is when peers ask you for advice and want to listen to you.
What is the best and worst thing about your job role?
It’s hard to beat the feeling of watching your latest model go live and users loving the product. I also really enjoy working with and learning from a wide range of skilled people, including business stakeholders and software engineers.
The worst part is probably that there are so many hypotheses to test, fun projects and endless amounts of interesting papers, that you simply don’t have time for everything.
What can you advise someone just starting out to be successful?
Make sure to first acquire the fundamental theoretical knowledge, which will help you make better model choices and communicate them with stakeholders.
Then, really try to master your tools and software engineering because it will enable you to collaborate with others, experiment faster and build robust systems for production. It’s valuable to talk with and seek to understand business users and objectives, but the most important thing is to find great people who can help you work and grow.
How do you switch off?
Nothing beats running in the park on a sunny day with some good music.
What advice would you give your younger self?
Learn to know when something is “good enough” and when details matter. Also don’t worry too much about getting what you think is your dream job on the first attempt. You will get there as long as you enjoy what you are doing and continue working towards your goals.
What is next for you?
My home is really important to me, somewhere I want to feel safe and recharge. I’m currently searching for a new flat in London, which isn’t the easiest thing in the world but I’ll get there. I’d also like to improve my tennis skills.
Career-wise, I’m focusing on improving discoverability and search on the 9fin platform in a new cross functional team, which is really fun.
If you could do anything now, what would you do? Why?
I would make sure the increasing power of AI benefits everyone, is applied safely and helps solve some of our biggest problems.
What are your top 5 predictions in tech for the next 5 years?
1. Private data will be the new gold, as widely available LLMs become aware of public data
2. We will start to debate energy consumption of generative AI more as usage increases during a crisis of climate change
3. Demand for more lightweight and faster AI models will increase as they enter smaller devices and reach more people
4. RAG systems will reach maturity and problems with the outdated knowledge of LLMs and limited context windows will finally be part of the past
5. Most jobs will still not be automated
Watch Hannes’ session with 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!