Cara Davies Out of Academia

5 min readFeb 14, 2022


Social Science to Data Science Series Guest Writer

The following blog post is written by Cara Davies. Nyx and Cara connected on the “Professor is Out” Facebook group. This is her LinkedIn if you’d like to get in touch:

**If you are interested in guest writing on my blog, please reach out!**

I knew, even when starting graduate school, that I never wanted to be an academic. I was terrified of teaching my own class and I always envisioned getting my Ph.D. and using it to leapfrog into higher government service levels, particularly since my sociology dissertation focused on race and immigration. However, when my second semester coursework introduced me to statistics and coding in Stata, I was enchanted. Coding came easily to me, as I had spent years in undergrad learning a variety of Romance languages and found a natural symmetry between plugging a new vocabulary into a proscribed syntax and plugging code snippets into an established pattern. Statistics and their potential for strengthening any argument also fascinated me, and I could see the potential for a lucrative analytics career outside of the academy. Therefore, I added an extra year onto my coursework and turned my Ph.D. minor in applied statistics into a second Masters degree, before beginning the non-ac job hunt.

I added my summer research assistanceships to my professional resume, as I had spent two terms involved in departmental research — the first summer conducting semi-structured phone interviews, the second supervising a team of graduate students conducting phone surveys. This gave me experience working with data from the very first phases of questionnaire design throughout the collection process, a perspective that few data scientists have. I also spent a summer working at the university’s business research center, where I had the opportunity to see how data was applied to solving business problems. With these three experiences on my resume — as well as comments about my overall time in graduate school, where I included the research projects I worked on, the conference presentations I gave, and the introductory social statistics classes I taught as evidence of my data communication skills —

I started applying to every data role I could find.

I’d like to say the job hunt was easy, and that my skills and expertise were instantly recognized. However, I spent close to six months applying to jobs and going on interviews before finally finding my first position. I had always thought of networking as synonymous with standing around awkwardly in a suit making small-talk with other desperate job seekers, but when an acquaintance from undergraduate mentioned that the analytics start-up he worked for was hiring, I reached out to him online to thank him for sharing the link to the job posting and to ask if he would recommend me. While I credit this minor act of networking for ensuring that my resume made it to the recruiter’s desk, my resume got me a phone interview. I was then given a practice data set and asked to perform an analysis with it and present it to a panel for my final interview, which I really enjoyed as I found it much easier to talk about my work than to talk about myself. I showed the work I had done and the assumptions I’d made, and explained what I would have liked to do if I’d had more time.

Shortly after I received the job offer and began my career as a data analyst.

Working at a start-up was a great experience, as I got to work on cross-disciplinary projects ranging from banking to healthcare to education, and I was involved in both client-facing and back-end work. I learned that many organizations lack even basic data literacy, and that sometimes things which might seem completely obvious to a third-year graduate student are overlooked or ignored by our internal liaisons. I learned that even the most sophisticated analytical solutions can fall flat without the right delivery: I will never forget the day that my team unveiled a predictive model for employee turnover using convolutional neural networks only for the CEO to reply, “We already knew that we could get people to stay longer if we just paid them more.”

Perhaps more importantly, I learned the importance of LinkedIn in this role.

One of my responsibilities included writing short blog posts about some of our projects for the marketing team to share, which I then re-shared to my own LinkedIn page as well. This allowed other people to see the work I could do in the business world, and the highly visual one-pagers were much more digestible than the typical academic paper. I had spent some time during my job hunt revamping my LinkedIn profile, but my colleagues encouraged me to share interesting articles I found and to connect with other data scientists sharing interesting content too. Soon, my LinkedIn page was attracting messages from recruiters for other companies in the area, and after only eight months at the start-up I joined a Fortune 500 company as a marketing analyst.

I found marketing analytics to be an extremely good fit for this social scientist.

While many questions were straightforward — how many new customers we had last month, or how many clients left — at their core, marketing questions are about human behaviors and social psychology. We had fascinating, data-informed brainstorming sessions about our client experiences, working to identify any pain points along the customer journey which we might see in the data as rejected offers or incomplete sign-ups. We talked with copywriters about how we should communicate differently to enthusiastic participators versus the disengaged, and we built models to segment our consumer population into these categories. Working with people data and try to solve a problem for our customer base kept me energized, well-paid, and with a work-life balance that I never had in my years of graduate school — after a long or frustrating day at the office, it was a relief to know I didn’t need to think about the problem again until the next morning.

These work experiences outside of academia continued to open new opportunities for me, and I doubled my start-up salary in three years through some lateral career moves. While I may not be using my Ph.D. credentials, the analytical and problem-solving skills I gained in my program were invaluable. I encourage everyone considering a career outside academia to get creative about applying their academic strengths to the professional world, to leverage their network (both online and offline), and to connect with others who share similar interests and career goals.

Life after academia can be fulfilling and fun.




Psychology | Data Science & Viz | Social Justice | Spanish