.cult by Honeypot is a Berlin-based community platform for developers. We write about all things career-related, make original documentaries .cult by Honeypot is a Berlin-based community platform for developers. We write about all things career-related, make original documentaries and share heaps of other untold developer stories from around the world.
This article was originally published on .cult by Louis Minvielle. .cult is a Berlin-based community platform for developers. We write about all things career-related, make original documentaries, and share heaps of other untold developer stories from around the world.
If you remember our past investigations and analyses, you might remember a curious postulate we put forward: even though their talents are in high demand, it might still be hard for data scientists, developers, and data engineers to know their worth. It’s almost like a brain-teaser: jobs requiring their aptitudes are expected to grow exponentially in the next three years, and high-skilled data science workers might know heaps about Python and the nooks and crannies of data science. Yet, in a swift-paced market and an ever-faster industry, they might not have an inkling about what salaries they should request in 2022.
To untangle this brain-teaser, we decided to analyse our data and create this report on data science salaries in Europe. This will also help employers get clarity on what the market looks like today in Germany, Austria, and the Netherlands.
Jump in and take a glance.
Expected salary vs. salary offered by company
There’s almost always a difference between expectations and reality. The expected salary and offered salary for data scientists is one of those special cases. Companies offer an average of €60.8k, and talents’ expectations for a starting salary are €60k.
How Salaries Have Evolved Over the Years
It’s always interesting — and important — to glimpse how the average offered salaries change over the years. It helps us understand if they fit the current times and are up-to-date, not only with other economic changes but also in comparison to other jobs. These are some takeaways:
There’s been an unmistakable rise in salaries from 2017 to 2021. (Please note that we don’t have enough data points to accurately examine salaries in 2020).
The highest salary jump between consecutive years was from 2017 at €55.3k as a going offer to €61k in 2018.
Salaries offered by companies in 2019 averaged around €59.3k. We suspect this was a correction due to a possible overenthusiastic rise and the job’s likely popularity. But this slight decrease seems to be an anomaly and should not be a deterring factor.
Salaries by years of experience
One of the many conditioning factors that’s considered when deciding what to offer is years of experience.
- There is a massive jump once data scientists reach two full years of experience, increasing salaries up to €60k.
- There is another increase once talent reach four years of experience, when the average offered salary goes up to €64.9k
On average, women earn 12% less than men
Overall, salaries offered to women throughout Europe are lower than those offered to men.
On average, companies offer men a starting salary of €62k, while they offer women only €55k.
Salary compared to company size
We found out that the highest salaries came from particularly small or large companies, while mid-sized companies offered similar starting salaries on average.
The average offer at companies with less than ten employees is €63k, while enterprise companies offer €65.k on average.
Data science salaries in Germany
We found that the averagesalary offered in Germany is €61.6k.
It doesn’t matter if you’re local
We found no significant difference between what companies offer to talent living in the target country as compared to elsewhere in the EU, or even in the rest of the world.
- Talent who live locally and are native speakers receive on average €61.7k, which is only slightly higher than other talent
Methodology and Conclusions
We used salaries specified by hiring companies during the interview process on the Honeypot platform as our key data source.
We removed interview invites sent with missing information (like position title or company location) to ensure that the data can be compared consistently. Furthermore, we also removed salaries that were unusually low or high to get rid of any extreme outliers. An external library was used to determine gender based on the individual’s first name.
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