How Much Does a Data Scientist Really Make?

compensation Mar 07, 2023

When you are looking for a job, there’s something that everyone wants to know about - compensation. How much you will be paid is an important consideration while job searching, but there is also a lot of hesitancy to discuss this subject openly.

However, I believe that avoiding the details on compensation makes it difficult for job searchers to know what to expect and how to judge offers, especially for those new to the data science industry.

That’s why in this post I hope to take some of the mystery out of a data scientist compensation package. I will cover both the various parts of a compensation package and the real numbers of what I made as a data scientist and software engineer.

(If you would rather watch me talk about this rather than read, I also have a Youtube video on this subject.)  

Basics of Data Science Compensation

Let’s start with the basics of data science compensation. For most data scientists, there is more to a compensation package than just a salary. There are stock offerings and bonuses to consider as well.

Here is what a typical offer looks like.


Salary in the data science space is the base level of compensation upon which other inducements (incentives) may be added.


One of those inducements is typically stock. Stock offered as part of a compensation package can be broken into three basic categories: shares of stock in publicly traded companies, stock options (usually private sales of stock to employees), and RSUs.

What all that means in short is that employees can either be gifted stock or given the option to buy it. Stock is usually dispersed over a number of years (the industry average is four).


Many companies will also add bonuses on top of the salary and stock to entice workers. These can be sign-on bonuses, annual bonuses, or retention bonuses. They vary in how often they are paid out or for what reason.

For example, a sign-on bonus is usually a one-shot deal to make signing a contract irresistible. An annual bonus is usually a percentage of your salary and can vary widely from company to company.


Besides these more financial considerations, don’t forget that there are other factors that come into play with an offer. Things like the company policy on vacation time and health insurance offerings can greatly affect your experience with a job.

Depending on your situation, these other factors might play a significant role in evaluating your offer, and that’s okay! I have friends who prize the ability to work remotely over anything else. As long as you know what’s important to you, you can evaluate your offers wisely.

So, to quickly summarize, a basic compensation package for a data scientist includes:

  • Salary
  • Stock
  • Bonuses
  • Other considerations like insurance

Compensation Depends on the Company

While that is the basic outline of a package, compensation does and will vary from company to company.

For example, a data scientist may do the exact same tasks at two different companies but be classified at a different level at each company. The level difference will result in a different compensation level.

Thus, it pays to do your homework when it comes to a company’s compensation structure. Just because you are doing the same type of work does not mean you will receive the same pay.

If you want to know more about how positions and levels compare across the industry, I highly recommend This website shows information about compensation packages (salary, stock, and bonuses) for tech giants such as Apple, Amazon, and Google. It even breaks things down by levels, which can be very helpful in evaluating offers. However, do be careful when considering this info as much of the data is user sourced and could be prone to manipulation.

How Much Can a Data Scientist Make?

Alright, now that you are familiar with what a compensation package consists of, what about numbers? What can you really expect to make as a data scientist?

Before I dive into my personal situation to give you some hard examples, I think it’s crucial to acknowledge that personal knowledge is limited.

I have a lot of friends and colleagues in the industry, and after speaking to them about their own compensation experiences, I can confidently say that data science compensation can truly cover a wide spectrum. I have some friends who only just cleared 100k and those that make half a million dollars. I even know of people who regularly clear a million dollars when all is told with their compensation.

I am going to share what I personally made because I believe that looking at a real example is the best way to understand what you can expect. Knowledge is power, but as we move forward and start looking at numbers, remember that at the end of the day, it really does depend on the individual and their situation.

How Much Did I Make?

Ok, so what about my personal situation? What are the hard numbers?

I worked as a data scientist at Airbnb. I began as a Level 4 Data Scientist and after about nine months in my original position, I transferred to a new role as a software engineer.

When I was first offered the data scientist position at Airbnb, it was for 310k. This is an all-inclusive number that takes into account my salary, bonuses, and stock options. I have to admit that this was a pretty fantastic compensation package, especially considering I had just been laid off two months previously—a low point I’ve touched on previously in another blog.

After this initial offer and hire, about nine months later I switched to an engineering team. Although I changed positions, the overall compensation level did not change. If you are interested in learning more about WHY I made this switch, I cover it in a previous video I’ve made.

There is still a little more to my story though. Airbnb went public in December 2020. Because I had been an employee with them since March 2019, I had already accrued stock options that now gained external value.

I’m not going to dive into the complexity of the rules governing the trading of these shares, but the end result was that after two years of working there, I was making over 600k per year. This drastic increase was largely due to to the rise in stock price.

That might make you want to quit everything and get a data science job immediately, but a lot of my journey came down to luck. I was lucky in my timing and the company I worked for. RSUs could also end up being worthless if the company never goes public. They can be very valuable (as they were in my case), but you need to remember that they might come to nothing when considering your offer.

Now, before we finish this look at compensation, I want to consider the other side of things. What did I have to spend to land that job and get that compensation package?

What Does It Cost to Be a Data Scientist?

It’s easy to focus on the great potential rewards for a data scientist, but there’s also a cost in terms of time and money to successfully get a job in the industry.

As you probably gathered from the rest of this article, data science pays very well, and because of this, it is an industry that attracts very bright and motivated people. Competition can be fierce and even after you land a job, the industry requires constant improvement—unless you want to risk being left behind by your peers.

I am not saying this to discourage anyone! However, I do want to give a sober reckoning of what the true cost of some of those very attractive data science compensation packages you see online may be. Knowing what you can get out of a job is one thing, but you also need to know what you will have to put in.

With that being said, let’s look at some of the costs associated with landing a data science position


Being a data scientist requires a lot of hard technical skills, and as such, the educational component can easily cost tens of thousands of dollars alone, not to mention the hours spent studying.

A master’s degree is becoming more of a prerequisite for many positions in the tech world, and I don’t need to tell you that going to school to earn these degrees takes a lot of time and money.

Still, if you don’t have a straight computer science degree, you don’t need to panic. I had a master’s degree in civil engineering before I landed my first data science position.

As long as you are willing to invest the time and money to gain the skills required, you’ll always have a chance, so don’t let a lack of an advanced degree stop you!

How did I gain the skills I needed to work in the tech world? I filled in my knowledge gaps using MOOCs, books, and other online resources (just like you are now!). From my own experience, I recommend that you prepare to spend a lot of time reading about concepts such as A/B testing and machine learning if you are not already familiar with them.

I also took several online courses through Udacity and Coursera, which I found immensely useful. If you are able to handle the online learning environment, I highly recommend their programming courses for filling in technical gaps. However, gaining access to this expertise isn’t cheap and can end up costing thousands of dollars altogether.


Money is the cost we tend to focus on the most, but it isn’t the only thing to consider if you want to job in data science. It will also require a significant time sink.

It’s difficult to estimate exactly all the time you will spend learning new skills, preparing for interviews, and maintaining your abilities.

For example, if you opt for a master’s degree, this could be two years of your life devoted to study, which is clearly a significant amount of time. This time is a cost just as the money is, and a sacrifice that you should be sure you are willing to make.

Learning online will also present time challenges. It can take months to fill in your knowledge gaps, and that could be on top of juggling another job and other responsibilities.

Overall, pursuing a career in data science can be wonderful, but it’s not something I would recommend deciding lightly. Whether you want to go to school to earn a relevant degree or are learning on your own using online resources, you should expect to spend a lot of both time and money to learn what you need to know to be a data scientist.

Final Thoughts

You can make big bucks as a data scientist, but it’s also a competitive field that tends to require some serious investment from you to land a job.

My goal in going over this is not to discourage anyone. I hope that you now have a realistic understanding of both what data science compensation looks like and what it takes to get those compensation offers.

Because finally, the truth is that even with all of the competition in the field, anyone can land a data science job if they work hard enough at it. You just need to look at my own experience to see that this is true. I got a master’s degree in a field unrelated to data science, got laid off, and then ended up with an offer amounting to 310k. If you want it, you can make it happen.

You can do it. You just need to be realistic about what it takes to do it.

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