The data scientist career path isn’t quite a universal experience. It can vary widely depending on what company you’re in. Although many industries may spout years of experience as a prerequisite of promotion, don’t get too hung up on this notion.
A common path adopted in the data science market starts with a more junior position, which evolves as experience is gained, followed by more senior roles, such as team leader or manager.
In general, though, to become a data science manager, most companies require around 5+ years of experience. At that point, most data scientists have had the time to gain some good work experience, and have generally been widely exposed to different teams and problems.
So, how much would you earn as a data science manager after reaching that level? Let’s dive into it.
Average Base Salary
Average Total Compensation
Seniority can increase the pay of a Data Scientist. Here are the base salaries of a Data Scientist grouped into 5 seniority categories.
Naturally, as you progress up the ladder in seniority, you’ll start making more money.
As a data science manager, you’d generally be looking at about a 30-45% increase in salary, compared to an entry level data scientist.
In terms of managerial positions in other data science related fields, data science managers command a greater salary compared to a number of other roles, such as data engineering or data analytics.
Data science salaries in the U.S. differ widely by state and city.
For example, in a city like San Francisco, you’re likely to get a higher paying job than anywhere else in the US. However, the trade-off is a much higher cost of living in San Francisco. Rental rates on average are more than twice that the national average. Grocery and transportation costs also rank higher than the national average. You’d have to decide whether that trade-off is worth it.
In general, jobs in metropolitan areas, such as New York, San Francisco or Los Angeles, are likely to offer you around 5-15% more than the national average.
On the other hand, cities in states with lower population density, like Raleigh, Austin, or Atlanta, present average salary estimates 8%-12% lower than the national average for data science managers.
When we think about high-paying jobs, it’s hard not to flash to FAANG automatically: Facebook (now Meta), Amazon, Apple, Netflix, and Google. Naturally, those employers will offer higher salaries, but there are plenty of other places to look for a higher pay.
Top companies have estimated salaries of over $200,000 per year, while the second tier of companies have salaries ranging between $160k-$190k dollars per year.
There is often a trade-off, though. Bigger companies may offer higher salaries, but they tend to have larger teams and more people working for them. This means higher competition for a promotion, and it may be more difficult to take the next forward step in your career.
On the other hand, smaller companies might not be able as high a salary, but they can offer you a fast track to the top. That said, the upward mobility can limited. After a certain point, there’s just no more positions available.
Data science is a growing field, with a lot of opportunities available.
You’d typically need about 5+ years of experience to become a data scientist manager, but when you do you can expect to be making between $100k to $200k.
Remember to take into consideration where you want to live, as well as the company you want to work for - getting the right job as a data scientist manager is about finding the right balance between all those factors.
To compare industry earnings, see our latest Data Scientist Salary report.