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 Oren:
I’m Oren Matar, until recently a Principal Data Scientist at Anaplan, and I’m based in Berlin.
I earned my Bachelor’s degree in Psychology and Humanities and a Master’s in Psycho-Social Studies at Birkbeck College, where my thesis explored the philosophy and sociology of science. While my journey to data science wasn’t traditional, it sharpened my scientific and critical thinking skills, which I’ve leveraged throughout my DS career.
Starting at a start-up called Epistema—where we used AI to map logical arguments—I moved on to Anaplan, where I spent the majority of my career. At Anaplan, I worked on NLP, auto-ML, supply chain solutions, and contributed to Meta’s ‘Prophet’ library for time series forecasting
How did you start out in your tech career?
Honestly, it was a bit serendipitous. I was working as a teacher when a friend at Epistema suggested I join their team. He saw potential in my background in Bayesian statistics and philosophy of science, and he believed those skills could be valuable to their work. Initially, I was hesitant—imposter syndrome was very real for me, and it lingered well into my career. But a conversation with another close friend encouraged me to take a leap of faith. She reminded me that life’s about trying new things, and that I could always return to teaching if it didn’t work out. Looking back, it was one of the best decisions I’ve made, and I’m grateful I took that chance.
What are the signs of success in your field?
In one sense, success in data science can be very tangible—often involving measurable improvements in model accuracy or key metrics. But I believe the true value of data science goes beyond just hitting numbers. It’s about critically questioning which metrics matter most and finding the right ones for each project. Success also lies in identifying where machine learning can drive value in unexpected ways. That’s why critical thinking is essential in this field: data science isn’t just technical; it requires a deeper understanding of the project’s purpose and the bigger picture.
What is the best and worst thing about your job role?
As a self-employed data scientist, I get the freedom to explore projects that genuinely interest me and to work at my own pace. The best part is diving into areas I’m passionate about without external pressures. However, the downside is working solo—I don’t have team members to challenge my thinking or fill gaps in areas where I’m less experienced. Another limitation is that this setup isn’t sustainable indefinitely. That said, I think taking time off to work independently is valuable; it’s a chance to build new skills and reassess what truly drives you.
What can you advise someone just starting out to be successful?
I advise anyone starting out in tech to develop a deep understanding of the techniques they’re using. For example, in data science, it’s crucial to understand how neural networks function under the hood. Take the time to learn their limitations, the assumptions they make, and explore alternative approaches. As you dive into specific techniques and algorithms, focus on the underlying principles they represent. This foundational knowledge not only enhances your creativity but also makes you more adaptable in your work, allowing you to tackle challenges with confidence and innovation.
How do you switch off?
Outside of data science, my greatest passions are 3D modeling and playing the piano. However, I often find it hard to fully switch off. I’ve become so immersed in the data science mindset that I tend to look for connections everywhere. For instance, I’m currently working on a project that uses machine learning to enhance 3D modeling. Even when I’m playing the piano, I can’t help but think about developing apps that could help me practice more efficiently. So, while I enjoy these activities, they often intertwine with my work in unexpected ways.
What advice would you give your younger self?
Let go of imposter syndrome—everyone experiences it, and the truth is, no one really knows exactly what they’re doing. As long as you’re learning, experimenting, and taking calculated risks, you’re on the right path. Additionally, seek out jobs in areas that resonate with your personal values or contribute positively to the world. There’s nothing quite like being part of something you truly believe in; it makes the work more fulfilling and meaningful.
What is next for you?
I don’t know! I’m taking some time off work to develop my own ideas for algorithms and applications, and I’m curious to see where they lead me. Throughout my career, I haven’t had the opportunity to delve into computer vision and reinforcement learning, so I’m using this time to focus on projects that incorporate these techniques. I believe in project-based learning, and I want to fully understand these areas by applying them in real-world scenarios. It’s an exciting phase, and I’m looking forward to the discoveries that lie ahead.
If you could do anything now, what would you do? Why?
I would assemble a team of experts to address two major global issues: the decline of democracy and the need for alternative protein sources. As a vegan, I believe the animal agriculture industry is one of the worst inventions of humanity, from environmental, animal rights, and health perspectives. Investing in alternative protein research offers exceptional value for money. Additionally, democratic institutions require modernization, and I’m particularly interested in promoting Citizen Assemblies—demographically representative groups of citizens who examine public challenges and develop policies. Like a parliament, but randomly selected. These assemblies have proven effective in empowering those who feel disenfranchised and bridging divides in our polarized society, as seen in successful implementations by governments in France and Ireland. There’s significant potential for innovation and technology in both areas.
What are your top 5 predictions in tech for the next 5 years?
While I know this may disappoint some, I believe we are currently in a significant AI bubble, that is likely to burst within the next five years. Self-driving cars have been “around the corner” for a decade now, but fail to make that final turn. With the rise of large language models (LLMs), we’ve heard increased promises of artificial general intelligence (AGI) and general-purpose robots. However, many in the industry overlook the inherent limitations of today’s AI methods.
Human intelligence is characterized by the ability to learn from minimal training and to generate creative solutions, whereas current technologies can only detect superficial patterns in data. This limitation is why they require vast amounts of data—to have examples to regurgitate in response to prompts. When subjected to rigorous testing in new and unseen circumstances, these models frequently fail.
Significant financial investments have flowed into the AI sector based on a flawed understanding of its limitations. While expressing skepticism was once considered heretical, an increasing number of voices are now acknowledging these limitations. When the bubble eventually bursts, I anticipate that the tech industry will need to reevaluate its direction and approach to innovation.
Watch Oren’s session with the Data Science Festival here.
Thank you to all our wonderful speakers for taking part in our Speaker Spotlight!
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