Ahmad Balkiss, data scientist and entrepreneur, reflects on his career development, from pandemic response to building meaningful AI solutions
1. Please summarize your data science journey to date.
I started my data science career in the middle of the COVID-19 pandemic.
With an academic background in public health and medical statistics, I got an exciting and meaningful opportunity to support the national effort for tracing the spread of the disease. As part of a great team at the Danish Patient Safety Authority, we utilised large scale datasets and trained forecast models to deliver insights needed by decision makers.
Since then, I’ve helped build various data-driven solutions in the public sector assisting public servants make data-based decisions. And in the past two years, my focus has been in increasing transparency and governance around the use and performance of LLM applications in the public sector.
Today, I’m taking the next step in my career by starting a new data and software company with a business partner, where I will continue applying data science to solve practical challenges in the public sector, while also exploring new ways to combine responsible AI, transparency, and real-world impact.
2. What do you enjoy the most as a data scientist?
Working as a data scientist, I really enjoy the constant opportunity to learn something new and the wide variety of skills I get to apply and develop along the way. The field evolves so quickly that there’s always a new method, tool, or challenge to explore, and I find that continuous learning incredibly motivating.
What I find most rewarding, though, is seeing the tangible impact of the work. When the tools I’ve helped build are being used and make a real difference in people’s daily work. It’s especially satisfying in the public sector, where even small improvements can have a meaningful effect on the society.
I also enjoy the collaborative aspect of the job; working closely with colleagues from different backgrounds, translating between technical and non-technical perspectives, and seeing how data-driven insights can change the way people think and make decisions.
3. What advice would you give to your younger self in 2018 when you started your career in data science with a Masters degree?
When I first started pursuing a career in data science, I thought that the most impact I would make will be through training sophisticated models on very large datasets and employing complex algorithms.
I’ve since come to experience that the most meaningful work is about people, understanding their needs and earning their trust. This often requires simple, reliable solutions and lots of unsophisticated but time consuming groundwork: cleaning data, aligning expectations, and communicating clearly.
If I could give my younger self one piece of advice, it would be to focus less on building the “smartest” model and more on solving the right problem, together with the right people with the resources available at the time being.
4. As you start your entrepreneurial journey, what are the things that excite you about the future?
Starting a company feels like the natural next challenge, one that pushes me to grow not only as a data scientist but also as a leader and communicator. I’m excited to build something sustainable, meaningful, and grounded in the values that brought me into this field in the first place.
It also pushes me out of my comfort zone, and I am sure that I will have to acquire a whole new set of skills to succeed at this. I look forward to learning from all the mistakes to come, and to collaborating with my co-founder, my team, partners, and clients to create tools that are both technically sound and practically meaningful.
5. What are in your opinion the most important “ingredients” that the data science community in Denmark needs to thrive?
I can only speak from my time in the Danish public sector, where data science doesn’t have a long-standing tradition yet. I’ve observed that much of the current momentum is held back by uncertainty about regulation and the heavy workload that comes with ensuring compliance. Teams often spend more time discussing what might be legally possible than building and testing solutions.
For the data science community to thrive, we need three key ingredients: collaboration, trust, and clarity. Collaboration across agencies to share knowledge and tools; trust from citizens and decision makers; and clarity in how to interpret and apply data and AI regulation. If we can make the regulatory environment easier to navigate and foster a culture of openness and shared learning, we’ll free up energy to focus on delivering real impact and innovation.