I’ve written a lot about the Data Science profession lately – both in terms of how to break in and whether the analytics industry itself is vulnerable to the automation trend that it is spearheading.
Today we will cover the juiciest topic of all, compensation. If the market for data scientists is so hot, then just how much exactly are they being paid?
For the Impatient – Let’s Get Straight to the Point
Before I talk about data sources and all that good stuff, let me tell you the numbers first. Personally, I hate going into a meeting with my boss about my raise and bonus, but then we spend the first 30 minutes going over other stuff – get to the point man! So here it is:

The median salary across all years is $120,000. And as you can see from the chart above, when we break it out by year, the trend is pretty flat and steady around $120,000. Keep in mind that this median is just base salary and doesn’t include cash bonuses, equity, and benefits. So the median total compensation for data scientists is most likely a good amount higher.
Before we explore some more trends in the data, let’s figure out what exactly we are looking at first.
Where did the Data Come From?
I got my salary data from this awesome website that indexes the Labor Condition Application (LCA) data from the Department of Labor (DOL). Basically, when a company intends to hire an employee that requires H1B visa sponsorship, they need to file a LCA with the DOL prior to filing a H1B visa petition. This LCA contains company, salary, and job title data that is publicly available to all.
I web-scraped the aforementioned website (if you want to do it yourself, you can find my code here on my GitHub) for salary data on data scientists from the following areas (sorry, I focused on the U.S. West Coast because that is where I live and work):
- San Francisco Bay Area (San Francisco, San Jose, Cupertino, Mountain View, Palo Alto, etc.)
- Seattle (including Redmond for Microsoft)
- Austin
- Los Angeles (including Santa Monica)
Also, I focused this analysis just on workers hired as data scientists. So this analysis doesn’t include more experienced data science job titles like senior data scientist or staff data scientist, nor does it include data analysts.
After all these self imposed filters, I ended up with 2,818 observations.
Finally, please note that this is H1B related salary data – thus, the salary data that I used for my analysis does not include the incomes of U.S. citizens. As I have not seen any evidence to the contrary, I will assume that the data for U.S. citizen data scientists and green card holding data scientists follow the same general trends as in the H1B data.
More and More Data Scientists are Being Hired
Good news, the number of data scientists hired has gone up significantly over the past 5 years. Note that as I am writing this in August, 2019 still has a ways to go, hence the shorter column at the end (in brown).

While data science is definitely a trendy profession these days, it’s my personal belief that the upswing in data scientists hired over the past few years reflect another factor as well – a lot of companies are trying to jump on the big data and A.I. bandwagon. So teams at these firms that were previously known as Decision Analytics or Research are being rechristened Data Science. So roles that a few years ago would have been titled research analyst are now called data scientists.
I don’t think there is anything wrong with this though. There is nothing sacred about the data scientist title – if you are applying quantitative data in an insightful way to help your organization make better decisions, then you are a data science practitioner in my book!
But do keep in mind that not all data science jobs are created equal. The overuse of the data scientist title means that a data scientist at Company A might be spending 80% of his time in SQL while a data scientist at Company B spends his entire day implementing Machine Learning algorithms in Python. Justified or not, this is probably one of the reasons why data scientist salaries exhibit such high variance (the other obvious ones are years of experience, location of hire, company’s average level of pay, and whether or not the employee has an advanced degree).
Let’s take a look at the distribution of salaries via a histogram. Since the data is not that different across years, I have plotted all 5 year’s worth of data in the following histogram. The two black lines show the 25th ($102,600) and 75th ($135,475) percentile salaries and the red line shows the median ($120,000).

In case you don’t believe me when I tell you that the distribution has not shifted much over the years, we can use a box plot to compare salary distributions by year:

For reference, in 2015, the salary quartile boundaries were:
- 25th Percentile: $100,000
- 50th Percentile: $115,000
- 75th Percentile: $130,000
And currently in 2019, the same quartile boundaries are:
- 25th Percentile: $100,000
- 50th Percentile: $119,850
- 75th Percentile: $135,000
So salaries did go up somewhat, but not significantly. Also, as of this writing, data scientist salaries in 2019 are down when compared to 2018.
Who Pays the Most?
So where do you go to get the big bucks? Here is a chart of median data scientist pay by company ranked from highest to lowest. In this chart, I only included companies that hired 10 or more data scientists during my sample period – it’s not that helpful if I show you a company that employs one lonely data scientist even if he or she is making $200,000.
You have the usual suspects at the top – AirBnB, Lyft, Facebook, Apple are all paying $135,000 or more. You also have a few surprises. Who knew Ancestry.com was a major and high paying employer of data scientists (nor did I expect Walmart to be near the top – I always assumed low low prices meant low low salaries)?

In case you are interested, I graphed the biggest employers of data scientists (the ones that filed the most H1B petitions) in the chart below. As expected, you have big tech at the top. One noticeable omission from my list is Google. For whatever reason, they don’t show up much in the database for data scientists. It may be that the title Google uses is different – I will investigate this more in the future.

Until Next Time
I hope this was informative. While not a complete picture as this is based on just H1B data, the numbers I got line up pretty well with what I have heard anecdotally and seen on salary aggregator websites like Paysa.
At some point I will do a Part 2 where I will dive deeper into the data and also bring in numbers for the other data related roles like data engineer and data analyst. I will also check out trends within companies – for example is Facebook paying data scientists more and more as time goes on?
Until then, cheers!
Source: https://h1bdata.info/index.php
Read Part 2 of this Series Here:
How Much Do Data Scientists Make Part 2
More Data Science and Analytics Related Posts By Me: