WiMLDS Paris: What did we learn from our data science salary survey?

WiMLDS Paris
5 min readSep 21, 2019

Data scientist has been called the sexiest job of the 21st century, and companies compete for top talent with generous monetary offerings 💰. The media frequently cite prodigious salaries paid by the tech companies to the data, machine learning, and AI professionals.

Whereas we might aspire to become one day famous and rich 🤑 data stars, information about real salaries of real data scientists, whether they work in start-ups, large corporations, or public sector, is not easy to find.

There are some articles and studies, mainly talking about the US market. Also for the US, Salary Project lists salaries for data professionals, who mainly work for large companies, with a median salary of $ 150K. The Salary Project by Career Contessa, which focuses on women, gives the range from $ 50K to $ 140K, which is rather wide.

Job boards do not necessarily mention compensation 💸. Depending on where you live, practice differs. Some companies mention the range, others don’t provide any salary information. When salaries are mentioned, the numbers are based on employers’ initial offerings. Yet, this is not a full picture: a salary study by APEC, a French job agency for ‘cadres’ (senior employees, managers, and executives), found an average 10K disparity between advertised jobs and what people actually made when hired. The reason? About 30% of candidates negotiate salaries higher than was mentioned in the ad.

What are other sources? There is Glassdoor’s data, which is more accurate than of on the job boards, as it takes into account self-reported information; Glasdoor also indicates salary range for different large companies.

In France, a helpful spreadsheet of anonymous salaries for start-up roles set up by

lists a handful of positions relevant for data scientists. Check it out, and please add your own salary: sharing is caring. (On the same note, if you know other sources of reliable information, please let us know.)

To get paid what you are worth, the first step is to know your market value. That’s why we decided to find out how much 💶 our community members, majority of whom are data analysts, data scientists, and machine learning engineers, are actually making.

In March 2019, we held a salary negotiation workshop with our team member

(if you missed the workshop, you can check the summary with her slides, watch the video, and read her article on the topic). Natalie asked the audience to share anonymously three pieces of information: their age, job title, and annual gross salary.

We collected over 70 valid answers. Although the sample is too small to be representative, we found analysing it fascinating, and decided to share our insights with you.

  1. It was no surprise that data scientists in academia make less 🔻 than industrial researchers.

When it comes to salaries, most universities cannot compete with industry. Moreover, it is often impossible to negotiate salaries in rigid public sector settings. Our call to action for academic researchers is to negotiate beyond salary. Inquire about project money, book grants, conference attendance, choosing your team, time away in other research groups, sabbaticals … Be creative and come up with a list of non-salary items that would be of value to you and easy to accommodate for your academic employers.

2. After the age of 25 (entry level positions), age is not a good indicator of how much people in similar roles earn:

Data Scientist, 26 yo, 50.5K a year
Data Scientist, 29 yo, 42K a year
Data Scientist, 30 yo, 72K a year
Data Scientist, 40 yo, 60k a year

Salary differences could be attributed to the company’s size, years of experience (data science as a new field has a high proportion of career changers), or negotiating skills. (Hint: learn to negotiate!)

3. In our community, we found no significant differences in salary ranges between data analysts, data scientists, and data engineers:

Data Engineer, 24 yo, 40K a year
Data Scientist, 24 yo, 40K a year
Data Engineer, 27 yo, 52K a year
Data Scientist, 27 yo, 53K a year
Data Engineer, 31 yo, 54K a year
Data Analyst Senior, 31 yo, 55K a year

4. We found a wide range in interns salaries: between 14K and 24K a year (pro rata).

A workshop participant told a story of how her company was looking for a data science intern and offered a candidate the same salary she was making as an intern. The candidate (male) asked for 600 euros a month more, and got it without any further questions. She said: “The company asked me whether I was OK with his request, I said OK, and that was it. Easy peasy! When I was interning with them, it did not even cross my mind to negotiate my salary!”

The moral of the story? Negotiate your salary even if you are an intern. As a data professional, you have leverage.

5. The highest salaries are linked to managerial responsibilities 💼, with salaries around 80K for people in their 30s to over 100k for older people.

This raises a question about alternative promotion tracks for those data professionals who do not want to become managers and who prefer to remain individual contributors. The question is pertinent mainly for the companies, because they will have to figure out advancement opportunities for their most valuable senior employees in order to retain them and to keep them engaged.

If this is your situation, it is worthwhile to be aware of this fact and start having early conversations with your boss. Again, think creatively what you would like to have, both in terms of monetary and non-monetary benefits: working remotely 🏖️, conferences, training, learning opportunities, choosing your teams and projects….

In conclusion, we would like to thank 💖 everyone who attended the workshop and contributed their salary data. Salary topic is still a taboo in many environments and salary conversations are not easy. We hope that this article would help you to prepare better for your next salary negotiation.

The salary negotiation workshop starts at 53'33:



WiMLDS Paris

WiMLDS Paris is a community of women interested in Machine Learning & Data Science