Last summer, 20 talented interns joined our multidisciplinary teams across Population Data Science team. Since their internships, we were eager to reconnect with our interns and learn where they are now in their career journeys.
In this blog series, our interns reflect on their time with us, highlighting the valuable lessons and skills they’ve attained, their achievements, and their aspirations for the future.
Eniola Folarin
Project: Managing confounding effects in the MAGENTA project.
Looking back on your time with us, what are the key lessons and/or skills you have learned?
The most valuable lesson was learning to take ownership of my work while managing a project independently. I developed stronger research, communication, and collaborative skills. My presentation and public speaking confidence grew significantly through weekly progress meetings and presenting at the Showcase event. I learned that rigorous research combines technical expertise with clear communication, and that stepping outside my comfort zone builds resilience and professional growth.
Is there a specific accomplishment/moment from your internship you are especially proud of?
Presenting my research findings at the Showcase event was my proudest moment. Initially nerve-wracking, it became incredibly rewarding as I was able to communicate the findings from my project and got amazing feedback. This experience transformed my anxiety about public speaking into assurance that I can do hard things, like presenting research findings on any stage. It validated my growth from someone hesitant about mistakes to a confident, independent researcher.
How has your understanding of Population Data Science changed during/following your internship?
My understanding of Population Data Science has deepened significantly. I now appreciate how essential it is for identifying patterns, informing policy, and improving health outcomes at scale. I’ve learned how population-level data are linked, governed, and translated into evidence that can influence real-world decisions. This experience helped me see Population Data Science as a dynamic, impactful field with enormous potential.
In what ways, if any, has this internship influenced your career plans?
This internship has clarified the kind of work I want to pursue. Working on the MAGENTA project and seeing its impact broadened my interest in research and data-informed public health. The experience showed me that I enjoy project coordination, evidence synthesis, and collaborating with multidisciplinary teams. It strengthened my interest in health research roles and encouraged me to build stronger data skills, which I plan to continue developing as I move forward.
What advice would you give to others thinking of applying to the internship programme?
I would encourage future applicants to come in with an open mind and a willingness to learn. The programme offers a unique chance to gain hands-on experience and work with supportive colleagues. Take initiative, ask questions, and make use of every opportunity to build your skills. The environment is collaborative, so don’t be afraid to contribute ideas. It’s a truly rewarding experience if you engage fully.
Eric Zhang
Project: Dementias Platform UK Data Portal Research Projects.
Looking back on your time with us, what are the key lessons and/or skills you have learned?
I developed strong practical skills in Retrieval-Augmented Generation (RAG) workflows, prompt engineering, and evaluating LLM outputs. I also deepened my understanding of data quality, governance, and the responsible use of population-level data in applied machine-learning systems.
Is there a specific accomplishment/moment from your internship you are especially proud of?
I’m especially proud of building a functional RAG pipeline end-to-end — from data ingestion to retrieval and LLM response generation — and demonstrating how it could improve accuracy and transparency in real analytical workflows.
How has your understanding of Population Data Science changed during/following your internship?
I now better appreciate how population-scale data can inform decision-making, the importance of safeguarding patient information, and the need to carefully validate advanced tools like LLMs before applying them to health or public data.
In what ways, if any, has this internship influenced your career plans?
It strengthened my interest in working at the intersection of AI and healthcare. I’m now more motivated to pursue roles where I can design or evaluate ML systems for clinical, public health, or real-world evidence settings.
What advice would you give to others thinking of applying to the internship programme?
Be curious, ask questions early, and embrace hands-on experimentation. The programme offers a great chance to learn cutting-edge AI methods while understanding the real-world constraints of population data science.
Güner Mertcan
Project: Enhancing Longitudinal Data Science by Understanding Policy Impacts on Key Measures Over Time.
Looking back on your time with us, what are the key lessons and/or skills you have learned?
My internship has strengthened my technical confidence in working with longitudinal administrative data. I’ve gained a much deeper understanding of the practical challenges of preparing and harmonising real-world data. Just as importantly, I’ve learned how to approach analytical problems with greater independence and critical thinking, and how to frame findings in a way that supports policy-facing work.
Is there a specific accomplishment/moment from your internship you are especially proud of?
I’m especially proud of presenting my project at the internship showcase. Bringing together the analytical, contextual, and policy-relevant components of the project into a clear narrative felt like a milestone. It was rewarding to see the work resonate with both academic colleagues and representatives of the Welsh Government.
How has your understanding of Population Data Science changed during/following your internship?
Before the internship, I had a mostly conceptual understanding of Population Data Science. Working within SAIL has shown me how complex, detailed, and interconnected population-level data are. I now better appreciate how population data can be used not only to describe patterns, but to understand systems, track policy impacts, and support public-sector decision-making.
In what ways, if any, has this internship influenced your career plans?
The internship has reinforced my interest in using data to inform policy-making. Seeing how analytical work can translate into actionable insights for government has been hugely motivating, and I now feel much clearer about wanting to pursue a career where I can combine technical data skills with evidence-based policy development.
What advice would you give to others thinking of applying to the internship programme?
Be curious, ask questions early, and don’t be afraid to explore an idea even if you’re not yet sure where it will lead. Working with large administrative data can feel challenging at first, but the support from the team-and the opportunity to work on genuinely meaningful, policy-relevant research-makes it an incredibly worthwhile experience. It’s a great environment to grow both technically and professionally.
If you’re interested in applying for this year’s summer internship programme, visit: https://popdatasci.swan.ac.uk/about-us/summer-internship-programme/