One reason data science careers draw so much interest is: the compensation. Data scientists are well-paid. And that’s true for those 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 will I actually make as a junior-level data scientist?

Let’s start with some simple entry-level data scientist salary numbers.

According to PayScale, the starting salary for a data scientist in the United States is $85,149.

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 six figures per year.

Today, we’re taking a closer look at entry-level data science salaries, with a look at how location, skills and company size affects average starting salaries.

A Comparison of Entry-Level Pay

Entry-level data scientists earn 1.6 times the national average (which is $53,490 according to the Bureau of Labor Statistics).

Yet, even compared to similar job paths, the starting pay for data scientists is among the highest on average. For example, the average pay for a software engineer (entry-level) was $77,166, while average pay for entry-level data analysts was just $56,491.

That sounds good on paper, but what are you really likely to make? Let’s dive into some of the most important factors that can affect entry-level pay.

Does Company Size Affect Salary?

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 PayScale and Glassdoor user-reported salaries compared 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, whereas at a company like Booz Allen Hamilton (22,000 employees), average starting pay is just over $72,000.

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.

What Data Science Skills Pay More?

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.)

Here’s a look at which data science skills affect entry-level salaries:

But it’s not just enough to list these skills on your resume. Companies will test your proficiency in these skills during the technical interview process.

In particular, you’ll be asked to apply these skills to business cases, and solve problems using your advanced skills. This is why interview prep is so crucial for landing a data science job.

Prep for your interview with Interview Query's data science interview questions.

Which Locations Pay the Most?

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, in these cities, an entry-level salary will go much further:

Austin is by far the best place on this list if you want to get the most mileage out of your income. (Note: We recently wrote about Austin data science salaries, if you want to learn more.)

Estimating Your Average Starting Salary

You can predict what you’ll earn in an entry-level data science job, but there are many variables that will affect what you can expect to earn. 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 data science interviews are so important. A strong technical interview helps you convey your competency with a specialized skill - like Python coding or statistics - which 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, but also, learning how to showcase those skills on your resume and in interviews.