Today, we’re talking about data science salaries.
Obviously, this is a huge– and typically taboo– question to ask just anyone, so in the spirit of full transparency, I’ll be telling you how much I’ve made in all of my data science positions ever since I’ve graduated.
First of all, let’s cover some basics. Your pay will very much depend on the company you’re working for, with respect to their size, how much funding they’re willing to allocate to you, and lots of other factors. And it also depends on you– your experiences, your career progression, and how much money really matters to you (pertaining to the position you apply for).
For context on the figures I’m about to introduce, let’s take a look at some industry statistics. Today, Glassdoor lists the mean annual data science salary to be $112,000. Indeed has an average salary of $120,000, while PayScale comes in a bit lower with $95,000.
On the other end of the scale, levels.fyi has a data science salary median of $150,000, and an insane $238,000 median coming from those in the San Francisco Bay Area. Out of curiosity–and sheer disbelief– I tried submitting a fake salary of $400k, and they still accepted it, so who knows?
Summary of my career and salary numbers
Here's a quick summary of my offers throughout my data science career
|Seniority||Year||Company||Company Size||Base Salary||Yearly Bonus / RSU|
|Junior||2016||Jobr||Super Small||$80,000||%0.5 equity over 4 years|
|Senior||2018||Nextdoor||Mid-Size||$158,000||$20,000 + 5,000 shares over 4 years|
And then of course - here's my LinkedIn for reference.
Now, how did I end up increasing and almost doubling my salary over the last four years? Why did I take a pay cut after six months at my first job?
Here's my story.
First data science salary offer out of college
After graduating college in 2015, I had two different job offers waiting for me.
The first was from a company called Workday, and they offered me a $95,000 base salary and $60,000 over four years in terms of stock. For total compensation, this equals out to about $110,000 annually. The position they needed to fill was actually for a Software Engineer in Performance, which is ultimately–spoiler alert– why I didn’t end up taking the job.
The second offer came from Inflection, and they were looking for a marketing analyst, extending a base salary of $85,000 with a $5000 sign-on bonus.
So why did I end up taking a $25,000 pay-cut in accepting one offer over the other?
Well, when I interviewed at Workday right out of college, they actually didn’t ask me any technical questions. The interview was very casual– we laughed and joked around, and mostly talked about projects I had worked on in the past.
Ultimately, while I was really happy with the offer (making six figures out of college was insane to me), I thought that the marketing analyst role would be more helpful for later transitioning to data science, and I was also a lot more excited to learn from my boss at Inflection.
If you're interested in how I landed my first data science job out of college, check out my new grad data science guide and my youtube video on landing a data science job without experience!
New graduate data scientist position salary
So as a new grad, I was making around $85k a year in the San Francisco Bay Area, which is definitely a really expensive place to live. My take-home pay after tax and everything was around $4000 a month, so, as you can imagine, I didn’t have a lot of savings left over.
Very quickly, I realized that this was the minimum amount I needed to survive. As someone new to the workforce that hasn’t seen that much money before, it can be a little distorting to see those big numbers and having it end up being much less in real-life, but I saw this as a good opportunity anyway for a new grad.
For a realistic take on what kinds of salaries you can expect as an entry level data scientist, check out our article on how much entry level data scientists actually make.
However, after working at Inflection for around three to four months, I started to feel like the company wasn’t really for me. I wasn’t learning a whole lot, and my boss ended up leaving in the first two weeks, so I decided to leave after a few months to seek out other opportunities.
At this point, I was kicking myself for not taking that position at Workday, but I ended up taking a new job with Jobr, which is actually where I found the position. They reached out to me with a data scientist position (specifically working with the recommendation algorithm), and eventually an offer came in for $80,000 with 0.5% equity, so my base pay would be cut by $5k.
Equity works based on the evaluation of the company at that time, which was around $10 million. Jobr raised $2 million with that evaluation, so my 0.5% cut of equity meant that I was entitled to around $50,000 over the course of four years, with the possibility of that value increasing.
Inside my employment contract, they had also included a clause that if they had raised the Series A, my base salary would go up to $100,000 once that Series A had gone through.
At this point, I kind of felt like I’d be stupid not to take the job because no one else was really interested in hiring me.
Startup acquisition: How much did I make?
So here’s where a bit of luck came into play. After six months of working at my job, Jobr was acquired for $12.5 million by Monster, who was looking at my company’s Tinder-for-jobs kind of app. Using that value, you’d think that I’d hit the jackpot with my share of equity at around $62,500. Unfortunately, no.
If you’re unfamiliar with acquisitions, basically when a larger company consumes a smaller one, there's a lot of lawyers, brokers, investors, and other middle-men involved. All in all, you can expect around a third of the amount you’re originally supposed to get to go to a bunch of different people, even if it’s above the evaluation the company raised (as it was in this case).
Pre-tax, I ended up taking home around $40,000, rounding up my first year with Jobr to be around $150,000.
To summarize, I guess the lesson here is that it’s important to continually develop your skills, as that’s what you’re being hired for. Money comes later (with a little bit of luck!), but it helps to set yourself up for success first in your current environment.
So after acquisition, earnout was also given out. Basically, revenue targets are set for certain metrics, and if you hit those, you can end up earning more from the acquisition. With the way it was written out in my contract, I’d basically be able to receive another $40,000-$50,000 for every year after the acquisition.
We ended up forecasting that we’d hit around 90% of them, so my take-home ended up being around $150,000 annually after that first year.
In 2017, the whole company in Jobr negotiated a raise from the parent company, so I got bumped up to $130,000 base-pay, making my total compensation per year at that point to be $170,000.
My last data science salary at Nextdoor
In 2018, my third year out of college, I ended up switching jobs again. Everyone I had known originally at Jobr had left at this point, including my current co-founder Shane. The work culture had changed, and I really wanted to find another company that really fit and supported my needs.
I knew that my total compensation was likely to decrease unless I interviewed for larger companies, but my main focus at this point was finding a place that I really liked.
I signed up for Hired, and Nextdoor ended up reaching out to me after I set my base salary ($150,000) requirement. In hindsight, now that I’ve learned a lot more about negotiation and interviewing, I don’t recommend using Hired. Once you set your salary at a certain threshold, companies will end up using that when they’re negotiating with you, setting up a salary expectation that’s lower than what they can actually allocate to you.
In this way, it’s better to withhold salary expectations until the end of the interviewing process, but I still consider Hired a good place to get a job if you don’t actually want to go out there and apply for X number of positions.
So with Nextdoor, I went through the entire process (including the on-site interview), so I was having good feelings about the company when I met everyone. My offer ended up around $145,000, which is lower than what I expected given what I put in my Hired profile.
This was a huge indication to me that their intentions were to start lower so we could negotiate up. In the end, I negotiated up to $155,000 with a $20,000 sign-on bonus, so my total compensation for that first year was $175,000. My offer included some equity as well, but this is much harder to calculate with a long-term unicorn like Nextdoor, as you don’t really know when they’re going to exit.
After I left Nextdoor, I learned that my colleagues all were being paid a variety of amounts. One of my co-workers with the same amount of experience as I was made about $10K more at $165,000 base salary. Another one was making about $10K less and had a few years more experience than I did. This tells you how important it is to negotiate your salary!
Currently, I'm at InterviewQuery, definitely making less than $175,000 a year. If I were to estimate given my career progression if I had taken another data science job, I probably could have secured around $190,000 in base salary, or maybe even more if it were at Facebook, Google, or Amazon, etc.
Data Science Salary: Tips & Tricks
If you’re just about to be in the negotiation process or if you’re just looking to break in data science salary numbers, here’s some helpful rules that might change how you look at things.
Never compare yourself to others.
Honestly, this is just an unfair comparison overall. You would be doing yourself a disservice in trying to connect different data science salaries to other people who have different luck, experiences, and career trajectories.
Determine your own market value.
The general consensus is that you’re worth the amount for which you can be replaced by anyone else in the field. So, for instance, if you’re doing BI analysis as a data scientist, then you can essentially be replaced by a BI analyst at a lower salary.
In this way, it’s imperative to really understand what your role entails, as well as how you can grow, whether that involves becoming a manager, a technical lead, etc.
Figure out how much money means to you.
When I was walking in and out of work everyday, I didn’t feel like I was walking out of $100k, or $200k, or whatever that equivalent is per hour.
Understanding your goals (trying to retire or be financially independent versus being more focused on learning more) is incredibly important, as it dictates, ultimately, what kind of company you may end up working for, and as a result, the workplace environment and culture.
If you're curious about the differences between working in data science between companies, be sure to take a look at our Start Up v. FAANG Companies article on our blog.
You make more money with experience.
Make no mistake, this also definitely depends on your interview, as you need to effectively communicate how much value you can bring with your experience.
However, I’ve seen many cases where people have similar skills, but one person was older with more experience, and as such, was earning a lot more money. As it stands, experience is still the #1 metric for determining how much money someone should make.
This comes with caveats however. At one of the companies I was working at, a new data science manager came in from a reputable Silicon Valley company. He had passed the interview, was managing two of my data science co-workers, and reportedly making over $250,000 per year. But only five months later got let go.
Because he couldn't keep up with the work. His subordinates were doing way more work than he was, and we hadn't been stringent enough in the interview process.
This just goes to show, you can keep on sitting around and gaining experience and commanding a higher salary: but you still have to prove your worth when you switch companies.
Salaries won’t keep you in data science.
I know, I know– it sounds kind of unrealistic, right? Of course, money changes things!
But honestly, beyond the initial ease in being able to provide food and shelter for yourself, your internal happiness doesn’t really change as the numbers do. You still end up going to work everyday, and your routines will probably end up being the same.
The things that constantly change will be around your position, so work hard, and ultimately, be sure to enjoy what you’re doing.
Thanks for Reading
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