career

Reverse engineering your CV

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I recently discovered a great podcast called The Startup Scientist, of which I have now swallowed up all episodes – thanks to Daniel Lakens for the recommendation! The podcast is about treating your academic career as a start-up. I have made this comparison before when giving talks at career orientations but wasn’t able to distill this idea, so I was excited to discover that Dan Quintana did exactly that, so now I can add on to it in this post.

The episode I want to write about is Reverse Engineering your Career Goals. Although I do often say that my career so far is a series of coincidences, I do think this type of reverse engineering has played a part in it.

Towards the second half of my PhD I had a lot of doubts about continuing in academia – I really wanted to, but I was aware that it would be very difficult to get a position. I wanted to do a postdoc, but only if I had a reasonable chance of getting a position afterwards. I was not sure how to estimate this chance, so I used the approach below.

 

1. Find representative data

I first studied the CVs of Dutch professors I knew, and concluded that most of them received independent funding a few years after their PhD. But these professors were already professors for a few years, so I decided their situations did not apply to me.

Through the funder’s website, I found a list of people in related disciplines who received this type of funding that year. Since I was a year away from finishing my PhD, and these people got their PhDs maximum 3 years ago, the gap between me and them narrowed.

 

2. Generalize

After searching for CVs of my “prototypes”, as a good machine learner I tried to find patterns. Although the people were all quite different, and I only had a few examples (I wasn’t doing this automatically, although that would have been an awesome side project), it was easy to spot several things they had in common.

I did this during my pre-Evernote era so sadly I do not have notes on what exactly I discovered, but I do remember two things in particular:

  • All recipients had an impressive (at least for me) h-index. The minimum I found was 6, but the values between 9 and 12 or so were more common. This will vary per field, but for context, the professors I thought were not representative, would be in the 20+ category.
  • Many recipients had an extra “thing” that was different from most others, like their own company, an organization they volunteered for, etc.

3. Multiple time points

Next to looking at people’s websites, it’s also helpful to search for “Name CV filetype:pdf”. Using this method, I was also able to find CVs of the same people, but from a few years ago. This had several benefits.

While the current CVs looked quite intimidating to me, the “time travel” CVs were much more relatable. Instead of learning only about the “value” of a CV, I was now learning about the “gradient”, which would be easier to apply to my own situation.

Of course, it is possible to do this for any CV, by removing all the things from the recent years. But the nice thing about the real “time travel” CVs was that I also saw how people changed the way they presented the same thing, that both CVs already had. For example, a side project that might have been insignificant in the “time travel” CV, was described in more detail in the current CV.

4. Predict?

Now that you’ve estimated your function where the inputs are the CVs at different time points and the output is receiving funding, you could try to fill in what’s missing from your future CV, to get the same output.

Don’t forget that there are multiple solutions (so different inputs will lead to the same output) and noise (so the same input might lead to a different output). Since I’m on a roll with this analogy – there are also lots of other inputs that you might not even be considering yet (life and stuff).

I did not actually create any concrete plans of what I was going to do, based on this informal study. But I suppose that the information got deposited somewhere, and helped me make choices that would later point me in the direction of a solution.

CV of Failure: introduction

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Image by https://unsplash.com/@tersh4u

My CV of Failure

Here it is – my CV of failure, or “shadow CV”.

I first found out about the concept of a CV of failure from this article. After a professor from Princeton posted his CV of failures online, shadow CVs have been getting more attention on Twitter, under the hashtags #ShadowCV and #CVofFailures. And it’s getting very popular too – the same professor now added a “meta-failure” of his shadow CV getting more attention than his research.

I already wrote about various successes and disappointments during my PhD (during my 3rd and 4th years). To write those posts, I used an Excel sheet that I normally use for yearly evaluations. Here is a screenshot from my 3rd year as a PhD student:

excel_progress

The one thing you can probably guess is that green is something that was successful, and red is something that was not. Creating a shadow CV would be essentially compiling all the red parts, over the five years that I’ve been doing research. In the era of tracking everything from what you ate to what music you listened to, why not track failures as well?

The experience

Given that I already had all the data, compiling the CV was quite easy. It was exciting – I was curious whether my shadow CV would be longer than a professor’s. It was comforting – the list wasn’t too long after all, and the inevitability of the list expanding in the near future didn’t seem as daunting. I also realized that it was good to start failing early with travel scholarships, because I feel more prepared now for the larger failures that I encounter.

But most importantly, compiling the CV was motivating. I thought about whether anything would have been different for me if I had seen such CVs a couple of years ago. As many other PhD students, I was not very confident. There were many things I didn’t even dare to apply for. Sometimes senior researchers would tell me these thoughts are unfounded, that I should just apply, and that everybody gets rejected. Sometimes I listened, and sometimes got rejected, but sometimes got accepted, which ultimately gave me more confidence. I hope that seeing shadow CVs can help other students do the same: go for more opportunities, fail, and learn from it.

On choosing to do a PhD

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This week I gave a talk at the career event of my former student society for mathematics and computer science students, ‘Christiaan Huygens’ (CH). All the speakers were asked to talk about their work, and the choices they made to get there. As it wasn’t always my goal to do a PhD, I thought it would be good for the students to hear about the doubts that I had. And now, for the purpose of sharing N=1 experiences, I’d like to share these thoughts with a wider audience.

Choices

One of the courses I followed during my masters was Pattern Recognition (IN4085, for the readers from TU Delft). I was immediately sold. It seemed magical that reading licence plates, recognizing faces, or predicting a patient’s diagnosis, were all based on the same underlying principles. I followed all the courses I could find on related topics, and did my graduation project on a pattern recognition topic (here is proof).

I was convinced that my job had to do something with pattern recognition. But, I also wanted to somehow apply all the other skills that I had developed during my student union time. As many of my classmates were choosing consultancy jobs, I was excited to find out that there was something similar there to match both my interests: a data analysis consultancy job at a large company in Amsterdam. Note that this was 2010, and nobody was hiring “data scientists” yet, at least in the Netherlands.

Everything was going really well with that company. I had visited them to find out more about their projects and meet the team. I really loved the new environment, and I was convinced I would be hired after an interview. Shortly afterwards, an opportunity to do a PhD in Delft came up. That was also an environment that I loved, but it was entirely different from the job in Amsterdam. It was safe and familiar. After all, I thought that during my MSc project I had already figured out what research was about (cough cough). Fueled by the belief that you should always do new and challenging things, I felt that a PhD would be seen as an inferior choice.

Doubts

But, doubts also started to creep in. I could not define them fully at the time, but now I am quite sure the doubts were: (i) I wasn’t sure I would be challenged enough by the technical challenges of the job in Amsterdam and (ii) I thought I would be challenged too much socially — always looking professional, always interacting with people. All things that I enjoy, but perhaps not every day, and not coupled with a long commute.

Perhaps my biggest problem was that at the company, it would be part of my job to oversell things — make things seem more impressive than they are to clients, and come up with convincing arguments on the spot. This ability was considered very valuable among my classmates, and I imagine quite sought after by companies. I of course felt honored by the company’s belief I had this ability, because it wasn’t something that came to me naturally. So while I was excited and challenged by the prospect of developing this ability, it also felt like I would be betraying myself a little bit.

Best advice I received

One thing that helped me in my decision is a conversation I had with a former student. He had just finished his PhD and was switching to an industry job. The advice was to

  • compare concrete opportunities (rather than PhD vs not PhD)
  • consider the people I would work with and the daily tasks I would have to do
  • whether I could be myself, and how the position would fit in with the rest of my life

This cleared things up for me, and once I had accepted the PhD position, it felt like a huge weight off my shoulders.

I didn’t regret my decision for a second afterwards. I had a great time during my PhD because of the people that I worked with, not only inside the lab, but also other researchers that I met at conferences. I discovered I was very wrong about knowing what research is about! There are new, exciting and challenging things in my job every day, but challenging in a way that is meaningful to me.

I also had quite a lot of freedom, both in the ideas I pursued and in managing my time. I was in the office during regular work hours, but I appreciated being able to take a day off, go on holiday, or work at home if I wasn’t having the best day. I felt that I was being valued for my ideas, rather than for showing up. And isn’t being valued one of the most important components for job satisfaction?

Keep in mind

That is not to say that you should always choose a PhD when in a similar situation. There are huge differences in PhD positions as well! Not all researchers are nice people, and not all projects offer the same freedom that I had. I hope that this strategy — considering concrete opportunities, and staying true to yourself — should help you with the answer.

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