Timeline of Evolution of Data Science: A Year in the Life
In September 2016, I left the Netherlands, where I completed my PhD in Cognitive Neuroscience, to live in Berlin. I didn’t quite understand why exactly in Berlin and what I would do there. But I thought that if there is one city where unceremonious, unemployed, highly qualified people gather, it is Berlin. After months of exploring the city’s various bouldering venues and nightclubs, I decided that my new direction in life would be data science.
So I asked friends with similar backgrounds in data science what I should study. Here’s what I still recall from their advice:
- python
- SQL
- machine learning
So, by March 2017, I finished my free codecademy course in python. Later in October, I completed three SQL courses from codecademy. Along the way, I mastered machine learning and have now taken a free HPIopen course on this topic.
In June 2017, I felt ready to enter the labor market. The rest is best summed up by this figure:
Notice the increasing steepness of the curve. This reflects the growing frequency with which I have applied for jobs. In the beginning, I applied for two jobs a month. Towards the end, I took two jobs a day.
Application processes took an average of 18 days, but the variability was enormous (3 to 46 days). By November 2017, one specific 22-day application process with StackFuel resulted in my first job offer. At that time, I still had six applications, including three at the interview stage, but I refused all of them. By December, my transition to data science was complete.
Facilitating the transition to data science: blogging and freelancing
During my job hunt I was very surprised to find out that recruiters in Germany have the following profile in mind when they think of a typical data scientist:
- studied statistics/economics
- worked in marketing
- knows the ins and outs of machine learning
- does not necessarily know how to program
Notice how the ‘science’ part in ‘data science’ is irrelevant. I decided to completely abandon relying on my scientific credentials to get a job. They only play a minor role in the CVs I sent out. Everything I did to score the next job in academia was more or less in vain for scoring a job in industry. I won’t deny that that was a tough pill to swallow. But I did.
Instead, I figured that I needed to convince recruiters of my skills without referring to my academic work. I did this in two ways. First, I started this very data science blog as a portfolio of what I can do. At every job interview I attended I got the impression that it received at least a cursory glance by the interviewer.
Second, I went freelance. Through some contacts I managed to convince the Humboldt University to give me a small text mining and classification assignment, as a paid freelance job. I worked on this from October 2017 onwards. It was instrumental in convincing employers that I am a data scientist, rather than becoming one through their company. I really liked working as a freelance data scientist and I want to pursue this route while being an employee, but first and foremost, I wanted a job.
Dealing with rejection
Only 4% (1/25) of my data science applications resulted in a job offer. Compare this to the 1/3 rate I had when applying for a post doc in academia. (I turned down that one offer.)
All application processes consisted of two interview stages, on average 17.5 days apart (range 10 to 26 days). Often I got a technical or business task in between. Presenting it would form the basis for the second interview. How good was I to reach each application stage? This figure shows my data science application funnel, i.e. a markov chain with transition probabilities: 
The ideal path is the central axis. You move from start to two job interviews, possibly with a task in between, and on to the job offer. The dots at each stage become smaller, representing the decreasing number of applications which went so far. The arrows connecting dots are sized according to the probability of moving from one application stage to the other.
Notice that scoring a job interview at all is the biggest hurdle. Of the 28% (7/25) of applications which advanced to the first interview, most also advanced to the second interview stage. Half of the applications at the second interview stage got me a job interview or I withdrew from the application.
Again: scoring the first job interview is the biggest bottleneck to working as a data scientist. The entire application material you send to your future employer should be 100% designed to getting that interview.
Top tips for securing a data science job
- take free online courses to improve your practical data science skills
- forget about your uni achievements
- show off your practical data science skills on your blog
- work as a freelance data scientist
- optimise your application material for securing a job interview
- don’t be too choosy with your first job
Welcoming a new generation of data scientists
What I describe is just my own case. However, my ex-colleague Gwilym had a similar experience in London. You can read his story on his excellent data science blog here. So, who knows, my case might generalise to others.
My new job actually involves training new data scientists. StackFuel is an educational technology start-up involved in further education. I hope that this blog post can convince others that a career switch to data science is feasible. After all, even I managed it.
The figures were made in R. The script is here.
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