This as-told-to essay is based on a conversation with Anand Ramanathan, who works at Meta and lives in New Jersey. The following has been edited for length and clarity.
When I was choosing my undergraduate degree in the early 2000s, people told me to pick computer science, the hot field of the time. Instead, I studied physics, all the way to getting my Ph.D in 2011, because I really enjoyed it.
In 2012, after working at NASA for roughly a year, I was thinking about what was next, and I looked into fields like defense contracting. My cousin, with good intentions, advised me to downplay my Ph.D in applications, by not bolding it on my résumé and placing it lower down the page than my other experience. It shocked me to the core. I’d worked so hard and learned a lot of skills, and felt my Ph.D had to be useful somewhere.
In the end, I stayed with NASA for several more years before pivoting into the data science industry. Now, I work at Meta. I’m really glad I got my Ph.D and I’ve come to understand how some of the skills it helped me develop are valuable in Big Tech.
I wanted to do something with real-world applications
My Ph.D at the University of Maryland focused on cold atom physics and involved a lot of research and lab duties, which made it feel more like I was working full-time rather than studying.
When I finished, I wanted to do something with real-world applications and landed a postdoctoral fellowship at NASA. I spent six years there as a contracted research scientist, where I worked with a lot of data, which I really enjoyed. This became a bridge for me into the field of data science, and I also did a few related courses to upskill.
I saw how my Ph.D and research experience could be transferable to the data science industry, and between 2017 and 2024, I worked as a data analyst and scientist at Capital One, and as a data scientist at Audible. Since July 2024, I’ve worked at Meta, where I use data to improve products.
My Ph.D taught me to communicate complex ideas, and be skeptical and curious
There are a lot of talented people around me at Meta, and although I don’t use a lot of the physics knowledge I gained from my Ph.D today, I think it sets me apart in some ways.
First, it taught me to value skepticism and verification. I was so enthusiastic about my findings, but my Ph.D supervisors were very skeptical and careful about subscribing to anything I presented to them. Now, after I’ve checked my work, I’ll often ask colleagues or domain experts to review it before circulating it broadly, especially when a result is surprising or business-critical.
During my Ph.D., I also spent many, many hours writing papers, including my thesis that conveyed the technical depth of my work in a way that others could understand. I used these skills when writing summary documents for senior leaders at Audible.
And there’s no promise of a $500k job at the end of a Ph.D, so you’re motivated by your curiosity, and learn to ask a lot of questions, which has helped me navigate the fast-changing career landscape. When AI LLMs first came out, I played around with them and took a couple of courses. I now feel like I know the basics of how LLMs behave thanks to my ability to learn and adapt.
I’ve had to go the extra mile to show how my Ph.D skills translate
I’m grateful for what I learned from my Ph.D, but because it can be hard to explain how it can apply to a job, I feel like I’ve had to go the extra mile to communicate what skills I bring to the table.
I wouldn’t advise doing a science Ph.D as a career move unless you like the field and love what you’re doing. But if you have done one, know that you do carry value. You may just have to put in a little more effort to translate that value when applying for jobs.
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