Data Scientist vs Data Analyst Salary | Towards Data Science

IntroductionData ScientistData AnalystSummaryReferences
Introduction
Data Scientist
Data Analyst
Summary
References
This article aims not to compare roles as if one deserves more money or not, but is instead a guide allowing professionals in these two fields to assess against their current salary. However cliche, it is still important to remember these two things when asking for a higher salary: it does not hurt to ask, and sometimes, you will not get what you do not ask for. Please keep in mind that these are more general statistics, as you could be specific as you want to be to see what your salary should be. Instead, these values are instead a directional guide for you to use.
To some, these roles can seem very similar, while for others, they can seem vastly different; the same can be said for their respective salaries. There are a lot of factors that go into a salary like seniority, location, education, skills, negotiation, industry, company, and more. So, when factoring in these characteristics, you can see quite the range of salary per role. Knowing what you have to offer is important as that can ultimately decide what your final salary is heading into a job from the job interviewing process.
In addition to the above characteristics, there are some other parts of salary that can compose the total compensation package. These include the base salary itself, bonuses, shares, and other benefits. Altogether, you might have a much higher salary than someone else, but it can be somewhat misleading as some of those parts of the compensation package are dependent on how you or the company performs. Lastly, it is important to note that recruiters may not be fully aware of the market salary so it is critical you know what to ask for based on those mentioned features. With that being said, let’s dive into the salaries for these two popular roles.
I have created a recent article comparing data science to machine learning engineer salaries, so I will try not to be as repetitive in this section, however, if you have not read that article yet, I will still include some useful statistics and discussion here.
Here are some of the expected titles you can see as a data scientist that might have a significant change in salary as well:
Entry Level Data Scientist → Data Scientist → Senior Data Scientist
Lead Data Scientist — Data Science Manager — Data Science Director
In addition to these titles, there are also some seniority levels like I, II, and III.
Below, I will show the range of salaries by title with their respective years required or expected.
Keep in mind that these roles are based on a US average (based on PayScale [3]):
Average Overall Data Scientist → $96,455
Average Entry-Level Data Scientist → $85,312 (1 year)
Average Early-Career Data Scientist → $95,121 (1–4 years)
Average Mid-Career Data Scientist → $109,696 (5–9 years)
Average Experienced Data Scientist → $136,051 (10–19 years)
Do I agree with these numbers?
No.
I believe these salaries should be higher than what is reported here. For example, I believe the mid-career average should be what is expected for early-career (maybe around year 2, not year 1, and depending on other factors).
As you can see, there is quite the range for the same amount of experience, depending on where you live, with a rural city in IL being on the lower end, and San Franciso being on the high end (looking at random, rural, popular cities).
Next, we will discuss data analyst salaries, along with changing the location, seniority, and skills.
Data analysts can expect to see a big range in their salaries based on several factors. Some analysts might focus more on past analysis, or static data, where some analysts might focus on predictive modeling, more similar to that of a data scientist. Because of these differences per unique job, the salaries can increase significantly from early to late in your career, as well as from adding certain skills to your knowledge base.
As an opinion, and from personal experience, and as well what we will see later on, from real data from PayScale, if you see a data analyst job description that includes words/skills like:
Python, R, SQL, programming, A/B Testing, and predictive modeling (etc.)
You can expect the position to pay more than if the job description is more focused around:
Excel, PowerPoint/Google Slides, Tableau (etc.)
Let’s dive into the various seniority levels of data analysts and their respective salaries [5] :
Average Overall Data Analyst → $61,754
Average Entry-Level Data Analyst → $56,383 (1 year)
Average Early-Career Data Analyst → $61,179 (1–4 years)
Average Mid-Career Data Analyst → $69,251 (5–9 years)
Average Experienced Data Analyst → $71,512 (10–19 years)
This salary data is gathered from PayScale, and if you would like a more specific estimate, then you use the salary survey [6].
Do I agree with these numbers?
No.
I believe these salaries should be higher than what is reported here. For example, I believe that even the experienced level average is too low for an entry-level data analyst.
As you can see, all of these estimates are much smaller than that of data scientists. I also think these are on the lower end in general. However, if you live in a bigger city and have skills as I discussed above, then you can expect a much higher salary — that can match or exceed data scientist salary, as well will see later on with specific skills added. Let’s dive into some different cities, rather than just the US average.
Here are some popular cities and some rural cities with their respective salary averages for mid-career data analysts:
San Francisco → $98,627
Austin → $70,485
Los Angeles → $71,487
There is quite a range for the same seniority level of data analysts as we can see from above. Now let’s look at those same three cities and apply a filter that includes the various skills:
San Francisco → $110,000 — “Data Modeling”
Austin → $90,249 — “Python”
Los Angeles → $81,951 — “Statistical Analysis”
These differences are huge. These are the same cities, with the same seniority level, but with an added skill that is closer to that of a data scientist or software engineer. With this information, you can see how important just a few changes in your experience can lead to a significant amount of more money or a bigger salary.
Knowing your worth is important, and getting a better sense of the skills that you do have now, as well as the skills that you could have, can make a huge difference in your salary. With that being said, the same seniority level of data analysts or data scientists could match one anothers’ salaries or exceed them, based on the skills that you have.
To summarize, here are some key takeaways of data science versus data analyst salaries:
* Average US data scientist salary $96,455* Average US data analyst salary $61,754* Data scientists can be more predictive, while data analysts can focus more on past/static data* Several factors contribute to salary, the most important most likely being seniority, city, and skills
I hope you found my article both interesting and useful. Please feel free to comment down below if you agree or disagree with these salary comparisons. Why or why not? What other factors do you think are important to point out in regards to salary? These can certainly be clarified even further, but I hope I was able to shed some light on the differences between data science and data analyst salaries.
Lastly, I can ask the same question again, how do you see salaries being impacted by remote positions, especially when the city is such a big factor in determining salary?
Thank you for reading!
I am not affiliated with any of these companies.
Please feel free to check out my profile, Matt Przybyla, and other articles, as well as subscribe to receive email notifications for my blogs by following the link below, or by clicking on the subscribe icon on the top of the screen by the follow icon, and reach out to me on LinkedIn if you have any questions or comments.
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I have also written a similar article discussing machine learning engineer salaries versus data scientist salaries here [7]. This article outlines and highlights similar characteristics of each, respective salary. Keep in mind that for both of these articles, these are not my salaries, and are reported by PayScale, and other actual data scientists, data analysts, and machine learning engineers. So, these articles are in turn discussions around real data and are intended for you to better gain an understanding of what makes a role (in general), increase or decrease in salary amount based on certain factors.
[1] Photo by Ryan Quintal on Unsplash, (2019)
[2] Photo by Possessed Photography on Unsplash, (2018)
[3] PayScale, Data Scientist Salary, (2021)
[4] Photo by Myriam Jessier on Unsplash, (2020)
[5] PayScale, Data Analyst Salary, (2021)
[6] PayScale, PayScale Salary Survey, (2021)
[7] M.Przybyla, Data Scientist vs Machine Learning Engineer Salary, (2021)