Have you reached ‘peak academia’?

Have you reached 'peak academia'?

Many people believe there’s a correlation between the length of time spent studying and the job market – so that the longer you study for, the more your employability improves. I’ve heard this equation repeated by several researchers I’ve spoken to.

Unfortunately it’s only true up to a point.

Speaking from the perspective of an employer outside of academia, I’m interested in hearing about your relevant work experience, as well as about your qualifications. And the longer you’re in academia, the less direct work experience you’ll have, by definition.

That doesn’t mean you have *no* relevant experience, it’s just that you have less experience in my industry, and more experience of working in universities. Which means that both you and I have to work harder to determine how your qualifications and experience are relevant to the vacancy I’m seeking to fill.

So the (correct) assumption you had while you were in high school – that getting a first degree will improve your employability overall – doesn’t necessarily hold for subsequent degrees.

It’s better to think in terms of ‘peak academia’, when you have about the right balance of qualifications and work experience (e.g. part-time jobs and internships), for many graduate-level jobs. Go past that – in my opinion past Masters degree level – and there are diminishing returns for your employability outside of academia (for many subjects, at least).

That’s not to say that researchers aren’t eminently employable, as of course they are – but in my opinion the ‘halo’ effects of education start to diminish, the longer you’re in it (and many employers do see a PhD as education rather than as a job).

Think about the other candidates who’re applying for the same jobs as you, who are in their mid-to-late twenties or older – they’re going to have a nice track record of responsibilities and achievements in that industry. Which proves to the hiring manager that they can already do the job well. It’s this relative lack of direct work experience that starts to count against researchers, the longer they stay in academia – and qualifications don’t make up the deficit.

So let go of the idea that more degrees means improved employability. If you’re doing a PhD or post-doc, you need to start investing time and effort in a wider range of activities, to build up your employability. This can involve:

– Getting relevant work experience;

– Conducting informational interviews to understand more about your target industry; and

– Networking with the right people in that industry.

Check out my five-step process for finding a job outside of academia, for more details about the steps you should take.

Academia is just one of many job opportunities for PhDs

Featured image for article: 'Academia is just one of many job opportunities for PhDs'

There’s an old saying – ‘when one door closes, another one opens’. I always saw academia as first and foremost a big opportunity – particularly for someone like me, being the first person in my family to go to university. Higher study offered me the chance to learn more, to develop my knowledge and skills, while at the same time doing good work (teaching and research). After my Master’s degree, I took the opportunity to do a PhD and a post-doctoral fellowship, both funded by British Academy grants.

From the perspective of a post-doc researcher, a permanent academic job obviously looks like a good opportunity to take. In my own case, I was well-qualified and I had a strong track record in the profession already. So over the course of my post-doc I applied for many lecturing posts, and was interviewed five times at UK universities. Each time I was unfortunately unsuccessful and the post went to another candidate. As I neared the end of my post-doc, academic employment began to look less and less like a good opportunity for me, and more and more like a dead end.

In response, I began to look elsewhere for options, and private companies in the newly-emerging field of e-learning caught my eye. Here was another whole field of opportunity – offering not just job security, but also a much higher salary and the chance to join an innovative industry. After my fifth academic interview rejection, I embraced my newly-found opportunity, and I left academia to work for an e-learning company, building web-based training courses. This initial switch led on to my subsequent career as a people manager, project manager and consultant.

Reflecting on all this, I feel that it’s healthier to approach your career planning in terms of relative opportunities, rather than as your single-minded passion or vocation (as we’re often encouraged to do). I appreciate that employers want you to display your passion for, and commitment to, their line of work. That said, it’s important that your commitment doesn’t slide over into self-sacrifice and exploitation, causing you to end up working for less than you’re really worth.

That’s to my mind where I personally drew the line – I refused to take up part-time teaching in order to stay in the academic game. I recognised that what we might call the ‘centre of gravity’ of opportunity had shifted in my life, following those five unsuccessful interviews. Realistically, with some previously open doors now closed shut, academia no longer held the same level of opportunity as it once did for me.

So I encourage you to reflect on where your present career opportunities lie – has the boat sailed on academia for you too? That can be hard to acknowledge at first, I know. But it’s in your best interests to take a step back and review the balance of opportunities as they’re panning out.

And maybe you’ll conclude that you need to switch your job search to look at roles outside of academia too. If you do, we’re here for you – a whole community of doctoral graduates working in fulfilling careers, and who are sharing their guidance and experience with you. Do check out my resources page for more details of all the books, websites and podcasts that are available to support your transition into a new career.

Interview with Lisa Qian, Data Scientist at Airbnb

Interview with Lisa Qian, Data Scientist

In this month’s post we catch up with Lisa Qian, a Data Scientist at Airbnb, to find out what it’s like to work as a data scientist. Read on to learn about the impact data science has on Airbnb’s success, the programming languages they use on the job, and what researchers need to know in order to succeed in a corporate role.


A: Things happen very quickly and data scientists have a big impact (see answer to next question). At Airbnb, there are so many interesting problems to work on and so much interesting data to play with. The culture of the company also encourages us to work on lots of different things. I have been at Airbnb for less than two years and I have already worked on three completely different product teams. There’s really never a dull moment. This can also be a “con” of the job. Because there are so many interesting things to work on, I often wish that I had more time to go more in depth on a project. I’m often juggling multiple projects at once, and when I’m 90% done with one of them, I’ll just move on to something else. Coming from academia where one spends years and years on one project without leaving a single rock unturned (I did a PhD in physics), this has been a delightful, but sometimes frustrating, cultural transition.


A: A ton! As a data scientist, I’m involved in every step of a product’s life cycle. For example, right now I am part of the Search team. I am heavily involved in research and strategizing where I use data to identify areas that we should invest in and come up with concrete product ideas to solve these problems. From there, if the solution is to come up with a data product, I might work with engineers to develop the product. I then design experiments to quantify the effect and impact of the product, and then run and analyze the experiment. Finally, I will take what I learned and provide insights and suggestions for the next product iteration. Every product team at Airbnb has engineers, designers, product managers, and one or more data scientists. You can imagine the impact data scientists have on the company!


A: At Airbnb, we all use Hive (which is similar to SQL) to query data and build derived tables. I use R to do analysis and build models. I use Hive and R every day of the job. A lot of data scientists use Python instead of R – it’s just a matter of what we were familiar with when we came in. There have also been recent efforts to use Spark to build large-scale machine learning models. I haven’t gotten a chance to try it out yet, but plan on doing so in the near future. It seems very powerful.


A: Successful data scientists have a strong technical background, but the best data scientists also have great intuition about data. Rather than throwing every feature possible into a black box machine learning model and seeing what comes out, one should first think about if the data makes sense. Are the features meaningful, and do they reflect what you think they should mean? Given the way your data is distributed, which model should you be using? What does it mean if a value is missing, and what should you do with it? The answers to these questions differ depending on the problem you are solving, the way the data was logged, etc., and the best data scientists look for and adapt to these different scenarios.The best data scientists are also great at communicating, both to other data scientists and non-technical people. In order to be effective at Airbnb, our analyses have to be both technically rigorous and presented in a clear and actionable way to other members of the company.


A: Beyond taking programming and statistics courses, I would recommend doing everything possible to get your hands dirty and work with real data. If you don’t have the time to do an internship, sign up to participate in hackathons or offer to help out a local startup by tackling a data problem they have. Courses and books are great for developing fundamental technical skills, but many data science skills can’t be properly developed in a classroom where data sets are well groomed.

This interview was first published on the website Master’s in Data Science; thanks to Josh Thompson for permission to reproduce it here.