Blog
22.04.2025

Transforming policy through data: the public impact journey

Dr. Helena Bakic connects academic research with practical policy at the Bertelsmann Stiftung in Berlin. After completing her Master's in Data Science for Public Policy at the Hertie School, she now crafts systems for institutional data sharing whilst pioneering analytics and AI applications across diverse projects.

Drawing on ten years' experience in both academic research and government advisory, Dr. Bakic champions the application of data science for societal benefit. Her work demonstrates a firm commitment to employing analytical methods to enhance quality of life through concrete interventions.

Her portfolio encompasses project direction, university teaching and professional training, built upon a remarkable capacity to distill complex data into accessible insights for varied audiences. This blend of technical knowledge and communication prowess allows her to translate data science into measurable social value.

Could you share your journey from your Master's in Data Science for Public Policy at the Hertie School to your current role? What motivated you to pursue this path?

 - I have always been interested in using data for the benefit of people, our communities, and our countries. I really wanted to use research methods, data science, and AI to make a difference in places where it's relevant and improve life for all of us.

When the opportunity came to work for the Bertelsmann Stiftung, I knew I had to take it because that's most certainly a place where we try to do exactly that across many areas. We produce quality research and quality policy advice, and try to improve the world we're living in. I thought it was a very good fit for me and for what I wanted to do.

You have a remarkable blend of academic and public sector advisory experience. How have these two worlds influenced your approach to data science for social good?

 - I think both worlds have given me something valuable. I've spent about five years in academia working as a researcher, and I really enjoyed the time conducting high-quality research. We built theoretical frameworks and I think we uncovered very interesting processes. My work was focused on community resilience and recovery after disasters.

However, my feeling was that academic work is not always geared towards policy change. While in government, I gained a deep understanding of how we can actually provide policy guidance and implement interventions to improve the lives of communities and people.

Basically, I feel like knowing these two sides gives me the advantage of understanding the requirements of rigorous scientific work, but also knowing what's required to imagine and implement change, because that's what we want. We want to drive change.

In your experience, how can data science be effectively leveraged to address social challenges and drive meaningful change? Can you share maybe an example of a project where you saw this impact firsthand?

- I recall a project from my time at the Office of Human Rights and National Minorities in the Government of the Republic of Croatia, where we conducted research for some of the most vulnerable communities in our country, including Roma communities. This was the first study of its kind in southeastern Europe.

The project included observing and measuring the living conditions of communities using different metrics. This data showed very clearly for the first time which main barriers these communities faced. This was immediately followed by an action plan with a budget attached to it. Our data also served as a baseline to measure any progress over time. Therefore, to effectively leverage data science to address social challenges, we need quality data and buy-in from governments and communities.

You've managed large-scale, data-driven projects with multiple stakeholders. What are the key challenges you found in those projects, and how do you navigate them?

 - I think big or small projects have the same foundational challenge: you need to understand the needs of the key stakeholders. Sometimes we don't invest enough time in this. I think that's important through every stage of the project: to proactively test whether the solution really meets the needs. We should do it often, being proactive and dedicating time and resources to adapt our vision of the project to the actual requirements.

Data science and AI are rapidly evolving fields. How do you stay up to date with new technologies and methodologies, and how do you incorporate this into your work for social impact?

- Things are developing so fast that I often have massive FOMO! We all have limited time to keep up with developments across the board. The best way for me to learn is from my peers. Networks like the Philea Data Science Group help me stay current. Through dialogue with colleagues, we can share experiences and exchange ideas. The importance of good networks cannot be overlooked in the learning process.

I noticed on your LinkedIn profile that you’ve worked in academia. From your perspective, what role does education play in shaping the next generation of data scientists?

- I think education is critical in shaping the next generation of data scientists. If we're talking about data science specifically, it's important to focus not just on technical skills, which most programmes do exceptionally well, but also on being ethically conscious.

Going back to the Hertie School when you were a student, what was this time like? What were the highlights of your time there, and maybe your favorite subjects or professors?

- I was part of a group of students who came to Hertie as professionals looking to work more on AI, machine learning, and big data topics. The courses offered were advanced and accessible for social impact work. I enjoyed the course on Deep Learning from Professor Lynn Kaack. I remember being amazed with the things we can now do with satellite imaging to understand our societies better. Hertie gave me the tools to achieve change and do so at scale, something I was looking for.

What kind of advice would you give to someone who would like to start in data science and maybe work in the public sector or for social change?

- Starting now is much better than starting ten years ago. Data science is more accessible today with so many tools available. Developing technical skills is key, and there are various schools and courses for that. Beyond technical skills, understanding policy, ethics, and communication is essential.

My advice would be to engage as soon as possible in real-world projects. For example, in Germany there are networks where students can participate in projects for social good. CorrelAid is one such network, which would be a great place to start. I also think developing strong communication skills is essential because a large part of data science work is communicating with stakeholders.

I'm actually wondering, what do you think should be done to attract more women to the big data and tech sector? As someone already working in that sector, maybe you have some thoughts about policies for schools, or generally how to make this industry attractive for girls and the next generation?

- It's a question very near and dear to my heart. I've thought about it really a lot, and I don't have a perfect answer. I think a big part of it is having mentors or peers who can actually support you getting on the right track. So mentorship is something that could potentially be a very strong tool.

I'm very often the only woman at the table, even in the non-profit sector. You can only really appreciate that when you are the only person of your type at the table!

I think what would have helped me throughout my career is mentorship from more experienced women in the field, and I am hoping to contribute as a mentor myself. It's really exciting to see more women coming into the field because there are so many biases in AI applications. That's why we need this diversity of different voices in the field.

What actually inspires you to keep pushing the boundaries of data science for social change? Are there individuals, books, or experiences that particularly influenced your journey?

- Early on, I read "Weapons of Math Destruction" by Dr. Cathy O’Neil. It's a visionary book about how algorithms can cause harm when used inappropriately. Debunking myths was one of the first things that inspired me to go on this path.

More recently, I've been following the work of Joelle Pineau, who is leading the No Language Left Behind project. As a native speaker of a small language myself, I find it inspiring that we can use AI to come together while preserving our language diversity. In the future, I hope we see even more use cases where AI is used to achieve social impact.

Thank you so much, Dr. Bakic, for sharing your inspiring journey and insights. It’s incredibly motivating to see how data science, when combined with empathy, policy expertise, and communication, can drive meaningful change. If you’re interested in following Dr. Helena Bakic’s work, you can connect with her on LinkedIn here.