Careers beyond industry: options in academia

One month ago we published report describing key requirements employers seek in ideal job candidates in industry. In this publication, we will try to undertake a similar research for academic jobs.

There are a lot of disparities in the lifestyle, the approach to manage projects and the working culture between academia and industry. Different way of work, knowledge and leadership skills are required from anyone working in academia or just preparing for a career in academia. In this publication, we enable data collected by NLP People to identify the most important criteria employers expect from candidates in academia.

Carrying on a good tradition, we asked Andrey Ziyatdinov, a full-time researcher at Hospital de Sant Pau (Barcelona), to help us with data analysis.

First, let’s have a look at how the distribution of postings by the areas covered by NLP People (NLP, Machine Learning, Data Mining and Localization) changed over time.

60-70 % of all jobs belong to the NLP field, followed by Data Mining (20-30%) and the resting two categories.

Proportion of jobs advertised in academia (261 jobs) and industry (2,074 jobs) across all categories can be seen in the following graph.

The portion of Academic job postings slightly increased from 10% to 15-20% in the last three months. By the way, NLP visitors can search jobs for specific sector using embedded tags at the bottom of the job description. For example, all academic jobs are available at this request:

In this report we focus on formal education requirements for the jobs at NLP People since this parameter differ tremendously between positions in academia and industry. In the next posts, we are planning to consider other factors, e.g. work experience.

In Academia, a PhD is desirable for the majority of positions. At the same time, the number of offers for the candidates with Bachelor is as small as for the candidates with Diploma (higher education of any level).


By contrast, the two most popular requirements to the candidates in industry are either Bachelor or Master degree.

The following chart shows what are the educational requirements that researchers and scientists in academia and industry must meet to get a job.

For both cases, the majority of jobs are offered to the candidates with PhD and Master.


Concerning Researchers/Scientists in Industry, there is a sufficient number of offers for the candidates with a Bachelor degree.

Digging deeper, we broke down jobs offered in academia and analyzed which education is demanded for each of the job types.

It is clear that although there are positions for candidates of all levels of education, a PhD level is mostly required. Obviously, job offers for Post Doctoral researchers and Professor positions require PhD, but there are also PostDoc offers that have requirements of Master (typically for candidates who are nearly defending PhD theses).

So, if you decide to start or continue a career in academia, having a PhD is very much desirable. The same for Researchers/Scientists in both sectors. However, if you are looking for a position of a Researchers/Scientists in Industry, the number of job offers for Master and PhD graduates is almost the same.



The graphics presented here are javascript-based charts available in the NVD3 library. We used the rCharts package for the R statistical environment that allowed us to create and customize the graphics in R, releasing them in the javascript format.


About the authors


Maxim Khalilov, PhD is the R&D manager at TAUS B.V and the co-founder of He is a former post-doctoral researcher at the University of Amsterdam, intern at Macquarie University (Australia) and a PhD student at the Polytechnic University of Catalonia (Spain).




Andrey Ziyatdinov is a full-time researcher at Hospital de Sant Pau (Barcelona) working very close to the bioinformatics data processing on a daily basis. He is currently pursuing the PhD degree in Bioengineering at the Polytechnic University of Catalonia (Spain). Andrey’s research interests include applied pattern recognition focusing chemical sensors, machine olfaction, and bioinformatics.



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