Computational social science is an emerging interdisciplinary field that leverages modern statistical computing and data science techniques to analyze social data. While jobs with the "Computational Social Scientist" title are still few and far between, there is growing, high demand for the CSS skillset in any industry that uses data to study human behavior, communities, and societies.
In 2025, that roughly translates to "everywhere."
Pursuing a career in a new, rapidly-changing space can be challenging, especially early in your career. The computational social science community is still small, making it somewhat difficult to find information on navigating the field. There are only a few dozen CSS-specific graduate school programs. Many of these programs are very new, and most are located outside the United States. Additionally, early career roles for new college graduates are often narrowly-defined, expecting applicants to identify as purely technical or non-technical. This framework can be limiting to those with more versatile, interdisciplinary skillsets, which are, ironically, extremely valued at the senior/managerial level!
Why Computational Social Science?
Despite these challenges, pursuing a career in computational social science can be extremely rewarding to those who aren't afraid to forge their own path. Each of the challenges above has its tradeoff.
Be early
First and foremost, there is immeasurable value in entering an emerging field early--ask anybody who worked in the software industry in the 1980s or 1990s. While other industries might have a clear, established path to entry, this introduces competition. Despite how many YouTube videos you can find about applying to be a software engineer in Big Tech, steep competition forces applicants to look for unique ways to separate themselves from the masses anyways.
Be flexible
Second, computational social scientists can work almost anywhere. While it may not be as convenient as Googling "computational social scientist jobs", an education that combines subject expertise with qualitative and quantitative practical skills opens a lot of doors. Common roles for computational social scientists include:
- Data Scientist
- Business Analyst
- Operations Analyst
- Research Scientist
- Project Manager
- Financial Analyst
- Market Research Analyst
- Technology Consultant
- Management Consultant
- User Experience Designer
- Quantitative Researcher
Be irreplaceable
Finally, a computational social science skillset is robust in the face of widespread AI adoption. Artificial intelligence can write and debug code. AI can also sift through academic literature. It can do either of those things faster than any human, and cheaper. However, AI is far from replacing the reasoning ability that ties the research design process together: Formulating questions, designing experiments, interpreting analyses in context, and identifying the implications of findings. Computational social scientists benefit from using AI as a tool to facilitate menial tasks, like debugging code, without the direct threat of replacement.