When people hear that I left a Ph.D. program in Political Science for a gig as a machine learning engineer, the most common reaction is confused silence followed by: “what?”
It’s no secret that we’re facing a shortage of tech talent in Silicon Valley. As the industry makes efforts to reform antiquated immigration policies and invest in campaigns that encourage everyone to learn how to code, we should also look toward less traditional sources of talent.
The social sciences are undergoing a renaissance of sorts. Funding for traditional social science research is drying up, and researchers are turning to interdisciplinary projects for grants. Among top Ph.D. programs, computational statistics is required, managing databases and constructing SQL queries is standard practice, and writing data processing tools in high-level languages is common. It is not unusual to find researchers with backgrounds in social network analysis, complexity sciences, and machine learning. Taken together, these skills make social scientists prime candidates for roles as data scientists and analysts.
And yet, they’re not getting jobs. In fact, 30% of graduates from Ph.D. programs in the social sciences are unable to find a job upon graduating. From my experience, the lack of job prospects boils down to two problems: social science graduates don’t know how to market themselves in industry circles, and recruiters readily exclude applicants with a degree in the social sciences from technical positions. Changing the status quo will require attitudinal changes from both sides.
Traditionally, social science graduate students have transitioned into positions in academia post-graduation. With their growing skill set, they should consider markets outside of academia, and tailor their CVs to emphasize the quantitative skills they acquired during their graduate education. However, their value goes beyond just quantitative skills; social scientists are trained to design research experiments and interpret statistical analyses in environments where there are potentially hundreds or thousands of causal factors. This puts them in a prime position to distill complex systems into parsimonious models, a skill that is highly valuable in the industry.
On the other hand, recruiters should start giving social scientists a chance for technical roles. Granted not all graduates will have a background in engineering, but they often have sufficient technical proficiency to fill data analyst and scientist roles. Furthermore, we need to dispel the notion that a student who did research in a subfield of political science or communication did not employ sophisticated statistical methodology. In fact, social science research often yields a unique and invaluable blend of quantitative and qualitative skills that the tech industry should embrace.
One thing is certain – Silicon Valley has a knack for identifying industries ripe for transformation. Perhaps it’s time to start applying the same principals to talent.