The most popular question I receive as a Data Scientist recruiter is: What is the salary of a data scientist? Data science candidates want to know what salary they should be looking for in their next position, and hiring managers want to know what salary budget they need to pay to attract and retain the best data science talent.

I’m going to try to break this question down as simply as possible without getting into too many specifics. At the end of the day, there are literally dozens of factors that go into determining a data scientist’s salary, but most everyone I speak to are looking for a general range as a guide, which is my goal here. Please note that this is a base salary guide, not total compensation. I did this to avoid too many ranges.

Average Data Scientist Salary by Experience

Entry-Level to Junior Data Scientist Salary

• $100,000 - $120,000

Mid-Level Data Scientist Salary

• $120,000 - $150,000

Senior, Lead or Principal Data Scientist Salary

• $150,000 - $200,000

Manager, Data Science, and Above

• $200,000+

What Is Each Level?

Each of the above categories is purposely pretty general, but this is how I define each.

• “Entry-Level to Junior” is anyone 0-2 years out of school, and almost always coming out of a Master’s or Phd program from a STEM field.

• “Mid-Level” is a data scientist with 2+ years of experience. Usually, they have worked at one, maybe two, companies so far in their career, and they are at the point where they can join somewhere new and “hit the road running.” They have used a variety of machine learning models and methodologies, and given their experience, they are able to pick the right tool for the right problem without need for direction.

• “Senior, Lead or Principal” data scientists can be anywhere from 3-4 years on up to the level of pure management. This level means you are more likely than not mentoring or leading others, without the expectation of hiring, firing or performing performance reviews — in other words all of the parts of management that don’t involve soft skills. Many data scientists stay at this level forever because they don’t like management; some move on from here within a year, but this level is obviously the most varied in terms of who falls in this category, and thus what their salary is.

• “Manager and Above” data scientists are those I define simply as anyone directly managing one or more people, whether they are analysts, data engineers or data scientists. They are often overseeing multiple projects or products and will have the authority to grow a team from scratch or expand an existing team.

Factors to Keep in Mind

Geography

Now that we’ve gotten that out of the way, let’s get into some details here. Most of my clients are companies based here in New York City, and these salary ranges are going to be influenced by that. From my discussions with clients in other large metropolitan areas, these salaries are in line with their budgets, but for those in smaller cities or hiring remote, these salary ranges are anywhere from 20 - 30% higher than their budgets.

Whether you have an office you actually have to go to will also make a difference. With the work-from-home movement in full swing, many companies are offering remote opportunities to candidates with the expectation that they will be paid less in return for not having to relocate or commute to an office every day.

Industry

These salary ranges are in line with most industries: technology, media, banking, insurance, mid-level startups, etc . . . There are two areas where I’ve seen salaries much higher than this (anywhere from 25% - 50% higher): Hedge funds/private equity and large, public technology companies. However, as a note, often the base salary is not different, however the total compensation is much higher; hedge funds/private equity may often pay out as much as 100% of the base salary in bonuses at the end of the year depending on the performance of the company overall, and the publicly traded technology companies almost always have a bonus in the form of stock options or grants, usually with a vesting schedule over a number of years — 4 is the standard — that can run up total compensation packages to twice the base salary amount.

On the contrary, not everyone is in it for the money, or for the money right now. Companies in industries such as non-profits, research or government pay much less, with the understanding that part of your motivation is working for the better good of society. Also, many early-stage startups offer much lower base salaries with the expectation of much higher salaries down the road or the option to take equity to make up for the difference.

Education

I would say that nearly every data science candidate I place has a Master’s, and more likely a PhD, from a good school in a STEM program. Of course, not every school is made equal, and I’ve seen clients that will pay a premium for junior-level or non-experienced candidates if they are coming from a very prestigious university. These candidates will often see their salaries bumped up to the next level just for that very fact.

Conclusion

All companies and people are unique, and it’s impossible to stick any one person or title in any one category. Like anything else, the value that comes in consulting an expert in this area (for example, a data scientist recruiter), is your ability to talk through your unique situation, either as a hiring manager or a candidate, to figure out what you need to do to attract the best and right candidate for the role, or what you can do right now as a data scientist to set yourself up for success in whichever direction you would like your career to go.

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