CSS Journey

My Early Career after Undergrad

I started my career as a software QA engineer in Kathmandu in 2019. Engineering felt like the practical path: a way to build a stable career, support my family, and work with systems I understood. But during the COVID-19 lockdown in 2020, I began questioning whether corporate IT was the kind of work I wanted to keep doing.

That question did not come all at once. It appeared slowly, through the parts of my work that pulled me outside software itself: corporate social responsibility projects, mentoring at the office, public speaking circles during the lockdown, and conversations with people working far from the IT industry. I was still using the language of technology, but I was becoming more interested in the social worlds around it.

The clearest turning point came through my mother’s organic composting farm. She had started the farm in 2018 and was facing difficulties from several directions: limited formal education, financial pressure, weak family support, and the everyday uncertainty of running a small agricultural business. My first instinct was technical. I thought I could help by building an e-commerce platform for her using the .NET MVC framework.

My Path to Social Science Became More Visible

I built the platform, but I did not know what should come next. More importantly, I began to see that I had built it more for myself than for farmers like her. The tool reflected my assumptions as a programmer, not her actual practices. She relied more on in-person relationships, local marketing, trust, and occasional Facebook posts. The gap was not simply a missing app. It was a mismatch between a technical solution and the social conditions in which that solution was supposed to work.

Discovering Social Science through Postgraduate Coursework

That experience changed how I understood technology. For the first time in my programming life, I felt that something important was missing from the way I had learned to think. I started looking for spaces where I could understand people, institutions, and everyday practices more seriously. That search led me to the Open Institute for Social Science in Nepal, where I completed the coursework for a postgraduate (PG) diploma in research and writing in 2021-2022. The capstone remained unfinished when I later shifted my focus to preparing for graduate study in Germany, but the most important part of that experience stayed with me: ethnographic fieldwork. It pushed me to stop treating technology as a solution in itself and to look instead at how people actually use, ignore, adapt, or work around systems.

This was also where my interest in digital agriculture, or more broadly in digitalisation, began, though it was initially inspired by my mother’s farming experience. I often used paid leave from my full-time software engineering job while completing my PG program to visit field sites and understand how farmers were encountering digital agricultural tools in Nepal. That project became the basis of my capstone research, which I returned to in 2026 through the Advanced Residency program at my former institute to complete my earlier work. It remains important to me because it came from a real failure in my own technical imagination: I had assumed that building something useful was mainly a matter of building the right tool. Fieldwork showed me that usefulness is social, situated, and often mediated through other people. I have also taken some additional classes in recent months as part of the Data Lunch Program remotely, especially focusing on Bayesian statistics inspired by Prof. Richard McElreath’s Statistical Rethinking book.

Discovering Computational Social Science

In early 2022, I discovered computational social science (CSS), but I initially faced many challenges because I did not understand the field properly and did not have formal training in statistics or causal inference in Nepal. While there were some opportunities at my social science institute, I felt I needed a more curated pathway and a strong degree to make this shift.

My journey into computational social science started with my acceptance to a full-scholarship program at the Hertie School (MSc Data Science for Public Policy, 2023–2025, Data for Good scholarship). Over my coursework, I increasingly shifted toward questions about politics, platforms, and public discourse on social media, as I felt that this was a more concrete part of the digital governance area I had initially wanted to explore through my digital agriculture research. In my MSc thesis, I developed a multimodal framework for studying TikTok virality and political engagement in Nepal. Although the project started as multimodal prediction using machine learning, what further piqued my interest was the pattern of unequal political visibility that followed from my findings: how some actors gain attention, legitimacy, and momentum through platform dynamics, creator networks, and public frustration with established institutions.

Does My Engineering Background Still Play a Role?

My move into computational social science did not mean abandoning engineering. It meant changing what engineering was for. I still value programming, statistics, data pipelines, scraping infrastructure, and reproducible workflows. But I no longer see them as the center of the work. They are tools that can support research when the question requires them.

Over time, I have become more convinced that engineering, statistics, and data science are only some ways of observing society. They can help us see patterns at scale, but they do not replace theory, context, or interpretation. Computational social science is where I now place my work: using technical methods to ask social science questions, especially in contexts where data access is difficult, public discourse is changing quickly, and existing scholarship is still limited.

The engineering background still shows up when it needs to. But now it serves research, rather than the other way around.

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