We analyzed over 50,000 data science salaries.
There is base salary, which is a standard everywhere, but with the advent of stock options, restricted stock units (RSUs), annual bonuses, sign on bonuses, 401K matching, and an extremely tight labor market, data science salary numbers can be complex.
Average Base Salary
Average Total Compensation
We analyzed data going all the way back to 2015 and realized that salaries for data scientists have gone up as the field as grown.
Because the average across all time periods isn’t really that helpful, we tried to add a recency weighted scale that would adjust the salaries for the more recent additions. As you can see, in the last two years, the average data scientist salary is actually around $140K.
We initially used a linear recency scale - but as you can see it’s still averaged to pre-2018. So in the future we might us an exponential one to really validate our findings and show the most up to date accurate information.
Data science is a term to describe many different positions across a spectrum of roles. If we compare the salary of positions named “Data Scientist” across data engineers and machine learning engineers while also looking at other tech jobs like product managers and software engineers, data scientists fall neatly in the upper levels, but the variance is pretty high.
Data engineers and data analysts salaries have also increased by 35% over the past 5 years compared to a 6% increase by software engineers.
But most data science roles are just playing catch up. Data engineers and data scientists made on average around 155K for software engineers.
Machine learning engineers were still the highest paying out of all data positions, averaging $175K in base salary in 2021.
It’s undeniable that years of experience is by far the biggest factor to influence salaries.
While most titles don’t have seniority titles, we scraped data and combined seniority titles with years of experience to create buckets representative of how we would envision a regular career path forming:
Individual contributor route → Entry level, Mid-level, Senior, Principal, Staff, Architect, etc…
Manager route → Entry level, Mid-level, Senior, Manager, Director, VP, etc…
Here we’ve bucketed almost all individuals with 5+ years of experience or varying levels of senior individual contributor titles (staff, principal, etc…) into one bucket called “Senior Data Scientists”. This drastically reduces the complexity around understanding how different organizations set their own levels (is L1 or L3 the bottom?) and titles (is principal more senior than staff?).
|Seniority Title||Seniority Categorization|
|Entry Level Data Scientist||0-1 years of relevant experience|
|Mid-Level Data Scientist||2-5 years of relevant experience|
|Senior Data Scientist||6+ years of relevant experience|
|Data Science Manager||Manager in job title|
|Director or VP of Data Science||Director or higher in job title|
Seniority can increase the pay of a Data Scientist. Here are the base salaries of a Data Scientist grouped into 5 seniority categories.
We’re seeing that on average, once you have a few years of experience under your belt, the base salary and total compensation numbers skyrocket.
So just note that after one or two years at a company as an entry level data scientist, and you’re not getting a significant raise, it’s definitely in your best interest to try to move jobs.
Additionally the data suggests that many senior data scientists on average make more in total compensation than data science managers.
This is especially due to the number of senior data scientists on staff at larger FAANG companies where an individual contributor with 10+ years of experience can make tons of money given their leverage in increasing company revenue.
Most data science salaries vary a lot by location. But Covid-19 and the shift to remote work really changed how we value location based salaries.
For example here are the cities with the highest base salaries. And it’s no surprise that average base data science salaries in San Francisco and Seattle come in on top with at over 130K, respectively.
But with the advent of remote work, employers started making choices whether to normalize salaries across the entire U.S. or based on where you were living. So if we look at the best cities to live, normalized data science salaries in Austin, Boise, Arkansas, and Cincinnati are among the best.
Education has a clear affect on how much you can earn as a data scientist. A master’s degree in a data science-related field, for example, typically results in a 15,000 increase in base salary.
A master’s degree is increasingly a preferred qualification for tech jobs at FAANG companies. The most common fields of study for data science include data analytics, data science, statistics and machine learning. Here are some of the top master’s in data science salaries:
PhD are a preferred qualification for a number of data science specialized research roles. PhD programs in mathematics, statistics, machine learning and data science are all options to land research jobs in tech, finance and more. Here are common salaries by degree obtained:
Lastly analyzing the top companies that hire for data scientists, we can see the FAANG companies all around the top.