Experience

I spent roughly five years as a software and QA automation engineer in Kathmandu, Nepal. The turning point came during the COVID-19 pandemic. My mother had started a small organic composting farm, and when it struggled, I built her an e-commerce platform — the kind of thing I had done many times before. It failed. Not because the code was broken, but because I had no idea how to bring people to use it: why farmers like my mother would or would not adopt a new tool, how trust in a platform forms, what moves someone from curiosity to action. I could not troubleshoot a social problem the way I could a broken build. That gap — between what I could build and what I could not explain — was the beginning of this.

It led me to the Open Institute for Social Science in Kathmandu, where I completed a postgraduate diploma in research and writing and focused my work on Digital Adoption and Perception of Nepali farmers (capstone-still in progress). I found social science genuinely absorbing, but also frustrating — I kept looking for a way to connect it to the technical thinking I already had. That question gradually resolved itself when I encountered computational social science — though not because it felt familiar. If anything, the shift made clear how different social science is from engineering: the question comes first, and the method follows from it, not the other way around. Where the engineering background eventually became useful was more specific — scaling data collection, building reliable scraping infrastructure, making pipelines reproducible — the kind of work that underlies research but is not the research itself.

At the Hertie School (MSc Data Science for Public Policy, 2023–2025, Data for Good scholarship), I moved from building systems to asking questions about politics and platforms. My thesis on political TikTok virality in Nepal, and the work since — on disinformation, alternative media, and political mobilization — sits at that intersection: computational methods in service of social science questions, with attention to contexts where data access and prior scholarship are scarce.

The engineering background still shows up when it needs to. But it is now in service of research, not the other way around.

A complete record of roles, research engagements, and skills is on the CV page.