Entry Level Data Scientist Salary

One reason data science careers draw so much interest is: the compensation. Data science salaries regularly top 6 figures. And that’s true for professionals far advanced in their careers, all the way down to entry-level data scientists.

Chances are, if you’re nearing the end of a Bachelor’s or Master’s program, you’re starting to think about compensation. You’re probably wondering: How much do entry-level data scientists make?

The average base salary for entry-level data scientists is around $100K and it increases to $137K in average total compensation.

Does that mean you are guaranteed that much money fresh out of college?

Not necessarily. Depending on your skills, location, and company, you could end up making anywhere from around $60,000 to more than $130,000 per year.

Interested in how quickly you can advance in your career? Typically, data scientists are considered mid-level with 2-5 years of experience, and mid level data science salaries are $30,000 higher. After six years, you can expect to be paid as a senior data scientist, which results in a nearly $50,000 increase in salary.

Entry-level data scientists earn 1.85 times the national average (which is about $56,000 according to the Bureau of Labor Statistics).

Yet, even compared to similar tech career paths, the starting pay for data scientists is among the highest on average. For example, the average pay for a software engineer (entry-level) is about $110,000, while average pay for entry-level data engineers was about $101,000 per year.

The skills you bring to the table increase your worth. More specialized skills result in higher starting salaries. (This is one reason master’s and PhD programs increase your value; they help you build specializations.)

Most data science positions fall under different position titles depending on the actual role.

From the graph we can see that on average the Machine Learning Engineer role pays the most with a $127,722 base salary while the Research Scientist role on average pays the least with a $69,903 base salary.

It’s no surprise that location affects pay. Most would expect to earn more in Silicon Valley than they would in Kansas City. One reason: Cost-of-living. There’s a clear correlation between tech hubs with high costs of living and average starting pay.

High-cost cities tend to pay entry-level data scientists more:

But before you pack up and move to San Francisco, be sure to first consider salary vs. cost of living.

In the Bay Area, you can earn six figures and still struggle to pay rent. So one metric we looked at was the power of your salary by city (the average salary compared to the city’s cost of living index / rent index). Essentially, cities with high salaries and moderate costs of living, an entry-level salary will go much further.

Some of the best cities with high salary powers are:

• Austin, TX
• Portland, OR
• Seattle, WA
• Chicago, IL
• Baltimore, MD

When you jump into salary data, it’s clear that company size affects average starting salaries. That’s not too hard to believe. You can make more at a FAANG company than you could expect at a start-up.

Here’s a look at entry-level salaries for data scientists by company:

There’s a clear implication that larger companies pay better. At a FAANG company like Google (140,000 employees), entry-level data scientists make about $139,000 on average, and similarly at Facebook, junior data scientists earn about $143,000 per year in base salary.

Comparatively, a smaller company like Credit Sesame offers about $95,000 per year to entry-level employees.

One reason: FAANG companies tend to hire professionals with advanced degrees. And that reveals a truth about starting pay: A data science master’s or PhD instantly increases your worth (and skills/expertise), and thusly, the starting salary you can expect to make.

It’s a challenge to predict what you’ll earn in an entry-level data science job, because there are many variables that will affect what you can expect to earn.

Here are a few tips: Salaries are highly dependent on location; if you want to earn more you might consider a change of scenery (while considering cost of living, of course).

More importantly, though, starting pay is dependent on the value you can add to a company. The more specialized your skills - whether if you’ve completed an online data science course, have earned a PhD, or have in-depth Python experience - the more you expect to earn.

This is why practicing data science interview questions are so important. A strong technical interview helps you convey your competency with a specialized skill - like Python coding or statistics - and that gives you leverage when it comes time for salary negotiations.

Bottom line, if there’s one thing you can do to earn more when starting in data science, it’s leveling up your technical skills and learning how to showcase those skills on your resume and in interviews.