Photo by Precondo CA on Unsplash [1].

 

Introduction

 
The goal of this article is to show the differences and similarities between two popular tech roles. We will deep dive into the average salary in US dollars by city and skills as well.

Disclaimer: Like I have said for the previous articles in this series, 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 or expected salary. Keep in mind that these salary values are more general and no one website can be enough to assess your worth. It is the direction of these values that can be utilized as a tool for you to have when dealing with salary in the future.

Who is the target audience for this article?

Recruiters and hiring managers comparing different seniority averages, as well as different percentiles of salaries, in addition to different factors that can contribute to a decrease or increase in salary.

You yourself, as a current data scientist, or a person looking to switch roles into data science, in comparing junior vs senior titles. This information can be helpful for determining which role you would like to pursue. It can also be interesting to clear up some assumptions between these positions in regards to salary.

 

Junior Data Scientist

 

Photo by Nathan Dumlao on Unsplash [2].

 

In my opinion, it seems that junior data scientist positions are quite rare. This observation is interesting and seems anecdotally unique, but I think there are reasons for the lack of junior positions:

1. One reason could be the name itself. Perhaps, hiring managers and recruiters promote a data science title without the level of seniority attached, and hire based on how people do in the interview. For example, if they are a junior, data scientist I, data scientist II, or senior data scientist, principal/staff data scientist, data scientist manager, etc.
2. Another reason could be the company headcount/hiring capacity. It could be possible that a company only has funds for one data scientist, and they prefer to hire a senior one over a junior one. This difference can depend on if the company is smaller or larger, and their possible required projects.

With that being said, let's assume this is a role in your situation, and look at some numbers for the junior data scientist position.

Note: this salary information has been last updated on February 01, 2022

Here are some of the various levels of junior data scientists and their respective salaries [3] (rounded):

• Average Overall Junior Data Scientist → $67k
• Average 10% (percentile) Junior Data Scientist → $53k
• Average Median Junior Data Scientist → $67k
• Average 90% (percentile) Junior Data Scientist → $90k

Do I agree with these numbers?

Somewhat.

Even though this position is junior, the average seems a little low to me overall. However, you could be getting a bonus, which could make this salary + bonus great.

To get to the point of being a data scientist, you usually need a lot of educational experience and have already had a professional position in something like software engineering, product management, or business analytics, so you could be taking a pay cut with this number. However, if you are just out of school, and do not live in a city, it could be a good salary regardless of the possible bonus.

Some possible factors for affecting your salary (some actions you could pursue, not financial adivce)

• Location
• Graduate school
• Certifications
• Bootcamps
• Remote
• Skills
• Bonus
• Profit shares
• Including school projects and relating them in a more business-type sense on your resume
• Including skills you assume people think you have, you might think everyone knows SQL and that you can leave it off, but maybe you were the only applicant who did not include it on your resume, and you did not get to the next interview step, for example
• Online projects that show some or most of the end-to-end data science process, from a site like GitHub or Kaggle

 

Senior Data Scientist

 

Photo by Austin Distel on Unsplash [4].

 

There are a lot more data points that are used in this salary analysis, therefore, it is more indicative of what we might assume as the average, amongst other traits. The previous role had 55 salaries, while this salary profile has ~ 1,500. With more information/data, we can perform a deeper dive on city/location and skill impact on salary.

Here are some of the various experience levels of senior data scientists and their respective salaries [5] (rounded):

• Average Overall Senior Data Scientist → $127k
• Average Entry-Level Senior Data Scientist → $108k (≤ 1 year)
• Average Early-Career Senior Data Scientist → $123k (1–4 years)
• Average Mid-Career Senior Data Scientist → $131k (5–9 years)
• Average Late-Career Senior Data Scientist → $140k (10–19 years)

Note: this salary information has been last updated on March 02, 2022

Do I agree with these numbers?

Yes.

Keep in mind, the above numbers are based on the whole title of senior data scientist, and then how long you are in that specific role.

These numbers look much better, especially considering bonuses and profit sharing are high with senior roles.

Payscale includes these ranges as well:

• Bonus → $5k — $24k
• Profit-Sharing → $2k — $25k

To expound upon the effect of city and skills on salary, I will be including a few combinations of the US average senior data scientist salary so you can see some examples:

• New York, New York → $160,000 with Deep Learning
• Chicago, Illinois → $120,079 with Artificial Intelligence (AI)
• Los Angeles, California → $140,000 with Bid Data Analytics
• San Francisco, California → $157,277 with Natural Language Processing (NLP)

As you can see, switching up the location and adding just one skill can change these numbers significantly. It is important to take all these numbers with a grain of salt, and it is really ultimately up to you for what your ultimate salary is.

 

Summary

 
Salary can be taboo to discuss, but it is still important to discuss what contributes to a smaller or larger salary, whether that be years of experience, skills, or location.

Here is a salary summary of junior data scientists and senior data scientists:

• Average Overall Junior Data Scientist → $67k
• Average Overall Senior Data Scientist → $127k
• Several factors contribute to salary, the most important most likely being years of expereince, 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 junior and senior data scientist 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?

I am not affiliated with any of these companies.

Please feel free to check out my profile on Medium as well, Matt Przybyla, and other articles, as well as subscribe to receive email notifications for my blogs by following the link below, and reach out to me on LinkedIn if you have any questions or comments.

Keep in mind that for this article, the numbers are not my salaries, and are reported by PayScale instead, and of other actual data scientists. So, this article is in turn, a discussion around real data and is 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.

 

References

 
[1] Photo by Precondo CA on Unsplash, (2019)

[2] Photo by Nathan Dumlao on Unsplash, (2018)

[3] PayScale, Inc., Average Junior Data Scientist Salary, (Feb 01st, 2022)

[4] Photo by Austin Distel on Unsplash, (2019)

[3] PayScale, Inc., Average Senior Data Scientist Salary, (March 02nd, 2022)

 
 
Matthew Przybyla (Medium) is a Senior Data Scientist at Favor Delivery based in Texas. He has a Master's degree in Data Science from Southern Methodist University. He enjoys writing about trending topics and tutorials in the data science space, ranging from new algorithms to advice on everyday work experiences for data scientists. Matt likes to highlight the business side of data science as opposed to only the technical side. Feel free to reach out to Matt on his LinkedIn.