by Vincent Yabor
In the booming field of data science, it’s relevant to check out how much you can really make at an entry level position. Although anyone can search the median and average salaries, we will break it down by factors such as company size, skill set, and cost of living.
Let’s start with some simple entry level salary numbers. According to PayScale, the starting salary for a data scientist in the United States is $85,473. 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 as an entry level data scientist.
First, let’s get a sense of how that compares to the national average across all professions. According to the Bureau of Labor Statistics, the average income in 2019 was $53,490. As a starting data scientist, you could be making on average 1.6 times the national average.
That sounds good on paper, but what are you really likely to make? Let’s dive into some of the most important factors when searching for a career.
Does Company Size Matter?
How much of an impact does the company you work for have on your starting salary? Payscale’s website as well as Glassdoor has user-reported salaries for numerous companies that can be sorted by experience level. Here’s how average entry level salaries compare between some of these popular companies.
Bearing in mind that salary data from smaller companies was quite scarce due to the salaries being user-reported, we can nevertheless distinguish between larger and smaller companies based on salary. At a FAANG company such as Google, you could make over $139,000 as an entry level data scientist, whereas at a company like Booz Allen Hamilton, you are more likely to start out at just over $72,000. Data scientists at companies such as Google are more highly trained and thus able to have a higher starting salary.
Code, Code, Code!
Those with more experience can expect to earn more than those without strong coding backgrounds. But how much more? Payscale offers insight into how much one could expect to make based on skill set.
Let’s just say you’ll be pretty well off if you have a strong background in machine learning. On the other hand, if you’re only really comfortable with the program R, then you might want to start learning more Python. In general, higher paying skills are the ones that require more specific knowledge rather than something generalized like Excel and R.
How does a company gauge your proficiency and decide what entry level salary to pay you? It is not enough to just list these skills on your resume. You must prove during a technical interview that you can reliably solve problems and fully utilize these skills.
Some skills are more ambiguous, such as Big Data Analytics. This is a broad topic which is investigated by employers through a range of interview questions. The best way to become proficient in higher value skills is to practice as much as possible.
With some practice and dedication, you can become proficient in SQL, Python, Machine Learning, and more. For real data science questions that have been asked at some of the world’s largest companies, visit Interview Query.
Salary by City
Next, we’ll examine where in the country you should be in order to get the biggest bang for your buck. As you may know, some locations in the United States correlate with higher salaries. What many people fail to consider is salary vs cost of living. First, we’ll break down the entry level salaries by ten cities listed on Payscale’s website.
Hear us out before you pack up and move to California! You may get more money on paper from places like San Francisco, San Jose, and Seattle. But will you be better off in those cities? Many people making six figures in the Bay Area live paycheck to paycheck and sometimes struggle to pay rent. The numbers may be enticing at first, but you will think twice once you see the cost of living!
Numbeo provides data on cost of living indices relative to New York City. In order to make a fair comparison, we’ve divided each city’s entry level data scientist salary by the cost of living and rent index.
Austin, Texas is by far the best place on this list if you want to get the most mileage out of your income. We also take back what we said about moving to California, depending on what city of course. Although Atlanta has the third lowest salary of the ten cities listed, the cost of living is low enough to make that salary the third most valuable relative to the other cities.
Choosing the right place to live can be just as important as the company you work for or the skills you have.
If you want to learn more about an actual data scientist's journey (and accompanying salaries), check out this article from Interview Query's founder, Jay Feng, and his experience as a new graduate in the field of data science.
It’s up to you to predict exactly what your salary will be as an entry level data scientist. Do you want to earn an average starting salary or do you want to earn six figures? Are you willing to sacrifice some spending power in order to live where you want to? Are you willing to put in the work required to hone your skills and make yourself a more valuable asset to employers? And finally, are you willing to put location and skill set together so you can have a shot at a large scale company?
No matter the path you take, data science is an excellent and lucrative profession. Here at Interview Query, we provide the tools necessary for you to make those important decisions.