Moving from an economics PhD to the private sector (analytics consulting)
I left academia for the private sector half a year ago. I handed in my dissertation and started working as an analytics consultant at McKinsey & Company, a large international consultancy.
I receive many questions about how that’s been, so I hope a blog post might answer most of the questions in one place.
What I do
We are management consultants, traveling to clients and working on projects that range from weeks to months. We bring quantitative firepower to teams and work on projects such as quantitative marketing, optimizing route networks, health care analytics, fraud detection or predictive maintenance. We use many different techniques, such as random forests, time series forecasting, optimization, network analysis, webscraping or deep learning.
Our studies are often full analytics studies from the start, and then using optimization or machine learning (ML) is more fundamental than “sprinkles on the top”. But it’s true that most of our time and many of our highest-value activities are “simple arithmetic”, such as collecting the right data, cleaning and merging datasets, calculating KPIs or writing a dashboard. I also spend a lot of time on tasks such as writing presentations, maintaining Excel sheets and calls and meetings with clients.
Why I switched
I decided to give the private sector a try for several reasons:
- I really like coding. I’d like to get better at it and it’s something that might be valued more in industry than in academia.
- I want to apply data science in a real-world setting (not least to find out if its all just a hype).
- I found research very satisfying, but I don’t want to specialize on one topic. My dissertation touched many areas and the demands of academia would likely have made it necessary for me to be more focused.
- I wanted to try something new.
Advantages of the private sector
It’s a cliché, but the thing I like most about my job are the people. My colleagues are inspiring, fun and highly motivated.
People are very friendly and sociable in the business world and consulting attracts people with good “verbal and social” skills.
You can make fast progress when teams work well together. And it’s not as bad to sit in a team room at 1 am if you’re in it together.
3. Speed of projects
I like that projects move faster and at some point they are actually done and you move on to the next one. This has a diversification effect, so even if a project is bad, it’ll be over not too far in the future.
4. Diversity of projects
The variance between projects is huge, because they can differ along so many dimensions: Industry, function, colleagues, place.
For me, it’s a good way to learn about the data science landscape and to see where my skills might be most valuable.
5. Being challenged
In academia you pick your own subject, methods and execute on your own speed. But being thrown into new topics is also activating and stops you from becoming complacent.
Consulting encourages a strong feedback culture. It’s common to have feedback talks every two weeks. This is very helpful: It’s hard to judge on your own how you’re doing and it means there won’t be any surprises after the project is done.
And yes, you’re better paid.
Advantages of academia
In academia, I had the freedom to pursue my own projects and that’s something I miss in the private sector. Projects are initiated at much higher levels in the hierarchy and in junior roles you’re executing them.
In contrast, economics research is not very hierarchical. You are free to pick your topics and you pitch your ideas to anyone.
At universities, the outputs of your work are public. Almost everywhere else – and especially in consulting – you might have accomplished cool things, but few people will know about it.
Academia is much more like running your own startup. You bear the risks, but you also reap the benefits. The downside is that you’ll have to live with the existential fears: “Will I publish my paper well?”, “Will I get tenure?”, …
3. Following rabbit holes
Management consultants live and breathe opportunity costs. This keeps you on your toes to keep making progress towards the target.
But this absence of slack means that what you learn and what you code is determined by what your project needs right now. You don’t just play detective and work on whatever interests you. In academia, new knowledge is the goal and you have to try many ideas and projects to find what has potential. Most private sector jobs are not “unicorn jobs”.
4. Work hours
I don’t think that I worked much less in academia. But work vs. leisure is much more sharply separated in the private sector.
When and how you work is largely out of your control in consulting and that’s a downside. In research, you have self-determined work hours. You might think it’s an advantage to push hard during the week and then to have the weekend off, but - at least for me - that’s not true.
It’s maybe a bit exaggerated, but I would say that the private sector is more conducive to happiness, but research is a better way to find meaning (see here) in life.
How to apply
As economists, we’re very well prepared for data science jobs. We know econometrics, statistics, mathematics and we have internalized most MBA knowledge (e.g. accounting, corporate finance, marketing). We know how to manage long projects, work independently and be convincing in communicating our results.
However, we’re not obvious hires for data science positions. Many people conflate economics students with business majors and might not consider us quantitative enough. It’s not clear to employers that we know ML or that we can program.
I would recommend to learn R or Python. Nobody uses Stata or Matlab (or Julia, EViews, …). You should know one of the two languages reasonably well to be able to start coding on unfamiliar subjects quickly.
You’ll be expected to learn other languages or software soon, too. For example, I’ve learned VBA, Alteryx and Tableau since I started and none of that I’d used before. But I think it’s important to know R or Python, so that you’re productive from the start and that you can show that you can actually code.
On the ML side, the first step is to actually learn about it. An economics program doesn’t teach you machine learning, so you need to look elsewhere. I recommend online courses, checking for courses at your own computer science department or trying Kaggle competitions. A great way to learn about it and to show that you know it is to apply ML in your research.
Given that the outputs of our academic work are public and that you might have time to pursue side projects, it’s a good idea to create public artifacts. It makes sense to take this into account early in your PhD when choosing research projects. I wrote papers with text mining and webscraping, partly because I wanted to learn about these topics. Having written a paper like that is great for your application, because it’s a public output that proves that you know the subject. I’ve explained my patent paper in most job interviews and it was very helpful.
Because outputs in the private sector are not public, the next best proxy is your work experience and years in your PhD often don’t count. Public artifacts are marks of your skill that can compensate for lack of work experience.
When deciding where to apply, it’s important to consider the framework in which a firm operates. So if you work for a consultancy, then you’re a consultant first and a data scientist second.
I’ve enjoyed the whole experience and I’m happy with my choice. But there are also things I miss about academia.