I spent 5 years of my life getting a PhD, but like many others who’ve gotten this degree, I decided to leave academia after graduation. At the time, I explored several career choices but ultimately decided to take a Product Manager job at Google.
In this post, I want to discuss why I think it’s a great destination for people with technical PhDs and also dig into the “career ladders” that most tech companies have.
Quick note on terminology
I’m using “tech” to mean computer software and/or hardware technology companies. There’s also biotech, but given that I haven’t done anything bio-related since my high school science classes, I’m not really qualified to discuss that side of the world.
Also, the intended audience here is “technical” PhDs, i.e. those in the sciences and engineering. You can certainly go into tech with a PhD in French Literature, but the degree will be less of a benefit, and you’ll have to prove your technical skills in some other way.
Tech jobs are a great fit with technical PhDs because they both require a similar set of skills to succeed:
- Ability to break down large technical problems into smaller chunks, work on them over many years despite roadblocks
- Comfort with complex quantitative analysis, particularly when the underlying data are messy or incomplete
- Strong communication skills
In some cases, the degree might also provide knowledge that’s directly relevant for a job- for instance, if your PhD was in software compiler design and your job is on the compiler team at a company.
That’s great if you’re able to find such a fit, and it will definitely help your career at the beginning, but it’s not a requirement, and seems to be more the exception than the rule. My research, for instance, was on using game theory for resource allocation, but aside from a little bit in my first job, my work has been completely unrelated.
In addition to the natural skills fit, there are a number of other great things about tech, which apply whether you’re a PhD or not:
- Great salaries, benefits, and work environment (casual, friendly, flexible hours, etc.)
- Fast-moving, constantly changing, hard to get bored for too long
- Ability to build and create things that people actually use
Before digging into the details of various tech career ladders, it’s worth briefly discussing some non-tech destinations that are also popular among technical PhDs.
Corporate research labs
A few companies such as IBM, Microsoft, and HP, run “labs” or “research” divisions that are sort of a cross between a research university and a large corporation. Staff in these places do research, write papers, and present at academic conferences like professors but are expected to work on problems that are relevant to the businesses of their parent companies.
I briefly worked at IBM’s Almaden Lab (and hated it- details to be covered in a future post!) and have met a few people at some of the other labs in the SF Bay Area. I think it can be a good fit if you absolutely love research but don’t want to deal with writing grants, teaching students, or doing the other, non-research-related work of a professor. On the other hand, you have less freedom than an academic researcher, and the work can be more boring and slower-paced than what you’d be doing in a more conventional tech job.
The big, traditional management consulting companies (e.g., McKinsey) love hiring PhDs, particularly those from prestigious schools. I halfheartedly applied to a few of these jobs as I was finishing up my degree, but didn’t get any offers and then never considered them again.
From what I’ve heard, these jobs can involve a rough lifestyle (lots of travel and long hours) and the work is often boring and repetitive. Every person I’ve known who’s gone into consulting has left the profession after 2-3 years, which I think says a lot about it as a career path.
On the other hand, the jobs are considered prestigious, and they can serve as stepping stones into high-level leadership roles in lots of different industries, including tech.
Several of my schoolmates ended up as “quants” at hedge funds and other financial firms. I don’t have any personal experience with these jobs, but from what I’ve heard they can be extremely lucrative. I’m personally not very interested in finance or the idea of crunching numbers all day, but if you are it might be a career path to explore.
If you’re interested in finance, I’d highly recommend reading this memoir to get a somewhat dated (but still relevant) perspective on transitioning from academic research to Wall Street.
Once you’ve decided to go into tech, the next step is to figure out which “career ladder” is the best fit for you. The members of the various ladders collaborate and there’s often some overlap in the work, but the choice of ladder determines how the interview process works and how you’re evaluated once you’re on the job.
The ladder names and associated job titles will vary from company to company, but there are generally three main buckets that technical PhDs are hired into. The following sections describe each in more detail.
Engineers, whether in software (like me) or hardware, are the people responsible for most of the hands-on technical work. This work, to put it colloquially, is a mixture of creating new things, updating existing things, fixing problems in existing things, and/or managing people doing any of the previous.
In addition to being the most hands-on of the ladder choices, engineering is also the intellectual core of a tech company. As a result, it’s often the “power center” where decisions are made and upper management is drawn from. This is not always the case, though- at a few tech companies, it’s product or design that runs the show.
The downside of engineering, particularly at larger companies, is that you’re typically focused on a very narrow slice of the company’s technology. This gets better as you become more senior, but other ladders like product management offer more breadth starting from the beginning.
Another limitation of engineering is that very specific skills are required, e.g. coding for software jobs. Many PhDs don’t have the necessary background here or enjoy these activities enough to do them every day.
Product managers are responsible for ensuring the high-level success of the products they work on. They do this by talking to customers, writing up requirements documents, setting priorities for engineering work, and developing relationships with other teams inside the company, among other activities. The work can be all over the place, and really varies a lot based on the needs of the product and the structure of the teams you’re working with.
The nice thing about product management, and the reason that I went into it originally, is that it’s super flexible and broad, even early on in your career. As an entry-level PM at Google, I not only interacted with lots of senior engineers, but also got exposure to legal, PR, design, internationalization, research, and other specializations within the company.
The downside of product management, and the reason that I eventually switched into engineering, is that it’s less technical and more relationship-oriented than the other ladders. Some people love this kind of work, though, so don’t let me dissuade you if you have the right personality for it.
Data science, at least when called as such, is the newest of the ladder choices. Data scientists do quantitative analysis to support product development. The exact day-to-day projects vary a lot, but some examples here include designing and evaluating product experiments, building models to detect malicious user behavior, and forecasting the company’s future product demand.
When I started my career 12 years ago, most of the jobs in this space were much more specialized, e.g. “financial analyst” or “experimental statistician”, so I didn’t apply for them. Since then, the more general field of “data science” has developed and become broad enough that it’s a good fit for many technical PhDs.
Data science is the most quantitative and “researchy” of the ladders, so it’s probably the one where PhDs have the biggest advantage over those with non-PhD backgrounds. On the other hand, it’s usually less about building new things and more about analyzing existing things (e.g., whether an experiment around a product feature was successful), so I think the potential scope and impact of the work is more limited compared to the other ladders.
Another downside of data science is that many tech companies don’t have a data science ladder. Or, if they do, it might be limited to just a very small part of the overall business. This will probably improve in the future, though.
Applying and leveling as a PhD
Unlike academic jobs, most of the positions in the tech ladders above don’t require a PhD. Instead, it might be listed as “desired” or “a plus” in the associated job descriptions. The impact of having a PhD versus not having one in the interview process will vary a lot based on the company and specific position you’re applying for. You might be asked to give a seminar on your research, or, as is the case for many software engineering jobs, it might make zero difference in how you’re interviewed.
Once you pass the interviews, having a PhD should improve the initial level you’re assigned and the associated compensation you’re offered (see this previous post on levels). When I started at Google, for instance, my PhD allowed me to skip the first two rungs on the PM ladder even though I had little prior job experience. This put me several years ahead of new grads entering with only bachelor’s degrees.
Wherever you end up, you’re likely to be working with and working for many people who don’t have PhDs. It’s nice being special, and people will definitely notice your PhD when you’re applying for new positions, but once you’re settled in a job, having the degree isn’t a big deal.
Tech can be a great place for PhDs, and, although I’m a bit biased, I think it’s better than the alternative career choices for many people. If you’re finishing up a PhD or stuck in an unfulfilling academic job, it’s totally worth exploring your options in the space.