I have done a big part of my career as a data scientist in Prague. During this time, I worked for different companies as a full-time employee and as a contractor, both as an individual contributor and as middle management. I also helped hire, and sometimes also fire, candidates. This has given me a good overview of the local data science job market that might be useful to you.

There are roughly three tiers: international companies, companies focused on the local market and startups. Here are some notes about my experience with each of them.

International companies

There are many companies that are based in Czechia but their primary market is other EU countries or the US. These companies tend to have higher compensation, from 40-60 thousand Czech crowns for entry level, and 80-100 with 2-3 years of experience. The working language is usually English and friendly with foreigners. As the main business unit or the final client is abroad, sometimes this higher pay implies meetings in different timezones and traveling. It also means that the output of the work may not be visible, and one can feel detached of it, especially in consulting companies. Since these are usually big brands, they give you a resume boost.

Local market

These are companies that have main customers in Czechia. Some of them have strong data science teams, like telecom operators (O2, T-Mobile, Vodafone), but there are many others. In these companies the pay is usually below that of international companies. They may not necessarily be English-friendly, which could be a problem for foreigners. One advantage is that the work feels (and is) closer: you can see the outputs of your models in your everyday life. They are also more involved with the local community in general, sponsoring hackathons, events, etc.

Startups

Startups overlap with the previous two, but are an important part of the local data science job market, and are a bit different. They tend to have friendlier schedules, be more open about remote work and more relaxed in general. On one hand this makes the transition from university a bit easier for many, as many startups look more like a cool student dormitory than a traditional working place. Some of them tackle riskier projects (i.e. more technically fun), and tend to have more modern engineering practices. Among these startups there are both product companies and companies focused on professional services. This second category is a smaller version of larger consultancies: they have international projects and one gets to rotate from project to project. They tend to be more open to part-time arrangements as well. There is more risk in general, if the business model is not resilient.

If you want more tips on how to start your data science career, check my Get a Data Science Job (learndataskills.eu) course. You can also reach out to me directly here.