Machine Learning Career: Pros and Cons of Having a PhD
It is often said that data science jobs are for seasoned professionals, and many job ads still show a preference for a profile with a PhD, with years of experience. Yet, many corporate employers have been disillusioned about the value that a PhD brings to the company. Likewise, many professionals, especially among those who just completed a PhD and were offered their first job, find the work sometimes unrewarding.
A PhD may command a slightly higher salary initially, and may be required for a position in a research lab (whether private or government-operated). But for many positions, it may not bring an advantage. Corporate work can be mundane and fast-paced, and the search for perfect algorithms is discouraged, as it hurts ROI. In many companies, a solution close to 80% of perfection is good enough, and requires far less time than reaching 99% perfection, especially since the machine learning models employed are just an approximation of the reality. People with a PhD are not well prepared for that.
Here are some of the negative aspects.
- You may spend several years of your life working on your PhD, possibly in a stressful environment, with low pay, delaying buying a home, or getting married. Meanwhile, you see your non-PhD friends ahead of you in their personal life. If you married when working on your PhD, this could eliminate some of these problems.
- Some recruiters may say that you are over-qualified, that your experience is not really relevant to the job you are applying for (or too specialized), and that adapting to a fast-paced corporate environment might be challenging.
- If you land a job in the corporate world, you might find it menial or boring. You could be disappointed that the research you did doing your PhD years is a thing of the past, not leading to anything else. This is especially true if your hope was to get a tenured position in the academia, but can't get one despite your very strong credentials, due to the fierce competition. It can bring long-lasting regrets and nostalgia.
- You may be lacking some coding skills (SQL in particular), which put you at a disadvantage against a candidate with an applied master. Of course, it is always possible and desirable to gain these skills on your own (or via data camps) when working on your PhD.
- Your salary might not be higher than that of a younger candidate with a master degree and the right experience. Your cumulative wealth over your lifetime may be lower.
- Some employers (Google, Facebook, Microsoft, Wall Street, or defense-related companies) routinely hire PhD's to work on truly exciting projects. Some only hire from top universities and if your PhD was not from an ivy-league, you will be by-passed. That said, there are plenty of companies that will hire non ivy-league candidates, and I think that's a smart move. After all, I earned my PhD in some unknown university, and eventually succeeded in the corporate world.
For some, the pros outweigh the cons by a long shot. This was my case. I provide a few examples below.
- If your PhD was very applied in a hot field (in my case in 1993, processing digital satellite images for pattern detection), you learned how to code, played with a lot of messy data, and even got part-time job in the corporate world, related to your thesis when working on it, then you are up to a good start. In my case, solid funding for the research, and even data sets, came from governmental agencies (EU and others) and private companies (Total, for instance) trying to solve real problems. This adds credibility to your PhD experience. On the downside, my mentor was not a great scholar, but a good salesman able to attract many well paid contracts.
- If you earned your PhD abroad like I did, it is quite possible that you were paid better than your peers in US. In my case, my salary, as a teaching assistant, was similar to that of a high school teacher. And conference attendance (worldwide) was paid by the university or by the agencies that invited me as a speaker. Coming from abroad is sometimes perceived as an advantage, due to showing cultural adaptation, and in most cases, being multilingual and able to easily relocate in various locations if corporate needs ask for it.
- You can still continue to do your research, decades after leaving academia. I still write papers and books to this day. The level is even higher than during my PhD years, but the style and audience is very different, as I try to present advanced results, written in simple English, to a much larger audience. I find this more rewarding than publishing in scientific journals, read by very few, and obfuscated in jargon.
- There are great positions in many research labs, private or government, available only to PhD applicants. The salary can be very competitive.
- VC funding is usually contingent to having a well-known PhD scientist on staff, for startup companies. So if you create your own startup, or work for one, a PhD is definitely an advantage. Even when I started my own, self-funded publishing / media company (acquired by Tech Target in 2020, and focusing on machine learning), my wife keeps reminding me that I would have had considerably less success without my education, even though you don't legally need any degree or license to operate this kind of business.
Having a PhD can definitely offer a strong advantage. It depends on the subject of your thesis, where you earned your PhD, and if you worked on real-life problems relevant to the business world. More theoretical PhD's can still find attractive jobs in various research labs, private or government. The experience may be more rewarding, and probably less political, than a tenured position in academia. It goes both ways: it is not unusual for someone with a pure corporate / business background, to make a late career move to academia, sometimes in a business-related department. Or combining both: academia and corporate positions at the same time.
I wrote an article in 2018, about how to improve PhD programs to allow for an easy transition to the business world. I called it a doctorship program, and you can read about it here. I will conclude by saying that another PhD scientist, who earned his PhD in the same unknown math department as me at the same time (in Belgium), ended up becoming an executive at Yahoo, after a short stint (post-doc) at the MIT, working on transportation problems. His name is Didier Burton. Another one (Michel Bierlaire), same year, same math department, also with a short post-doc stint at MIT (mine was at Cambridge University), never got a corporate job, but he is now an happy full professor at EPFL. Also, a Data Science Central intern (reporting to me), originally from Cuba and with very strong academic credentials (PhD, Columbia University, EPFL) got his first corporate job after his internship with us (I strongly recommended him). Despite a mixed academic background in physics and biology, he is now chief data scientist of a private company. His name is Livan Alonso.
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About the author: Vincent Granville is a data science pioneer, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent is also self-publisher at DataShaping.com, and founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target). You can access Vincent's articles and books, here. A selection of the most recent ones can be found on vgranville.com.