6 Factors for a High Salary Data Science Career

The pay for a Data Science career is higher than in most industries.
By Claudia Virlanuta • Updated on May 2, 2023
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It is said that in business, as in life, you do not get what you deserve, but get what you negotiate for. This is as true for Data Scientists as it is for everyone else, and this, along with five other factors that I will discuss below, it will positively influence your Data Scientist salary.

Here are six factors that will help you optimize your Data Scientist salary.


1. How Your Industry Uses Data Science

All industries need (or will soon need) Data Scientists and other analytical talents. Data is generated by terabytes everywhere, and all organizations, big and small, could benefit significantly from having a Data Scientist on staff.

Indeed, Data Scientists are being recruited with ever-increasing frequency in every industry, in both the private and public sectors. While every industry has its pros and cons, the pay for Data Scientists is substantially higher in the following industries, per the most recent O’Reilly Data Science Salary Survey (Disclaimer: the survey information in this article is from 2018. The link will show information from the latest 2021 O'Reilly Data Science Salary Survey). 



Search and Social Networking

The companies whose websites you spend the most time on are also the ones who have the most data and the biggest need for Data Scientists. If you can get past their rigorous interview process, companies like Google and Facebook will pay a premium for top Data Scientists. According to Glassdoor, the average Data Scientist salary at Facebook is $133,000, based on 47 reported salaries, while at Google, the average is $138,000, over 5 reported salaries.


Banking and Finance

Judging by the way things have been going with the stock market these past few years, you’d think that the financial industry’s glory days as the first choice for top talent are behind us. However, banks and other financial companies are still competing for top analytical talent and offer competitive salaries to attract and keep the best.


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While manufacturing in the US has been getting more and more automated in the past few decades, it has also been getting a lot more data-savvy. From car manufacturers to the makers of clothing, electronics, and convenience foods, manufacturing companies are vying to become employers of choice for data science and analytics professionals.

For instance, according to Glassdoor, a Data Scientist at Lockheed Martin makes on average $102,000 a year, while the average Senior Data Scientist working for a defense contractor can clear $112,000 per year.



We now live in an age where software makes the world go 'round, so it is little wonder that software companies, big and small, are looking to gain a competitive edge by mining their data for profitable insights. In a highly competitive industry such as software, a company is only as good as the people it hires, so compensation tends to be competitive as well, particularly for valuable data science candidates.

Well-funded software startups in particular are known to pay their Data Scientists handsomely, and they're also widely seen as good indicators of longer-term trends in data science salaries. However, less-funded, or bootstrapped software startups can also be a good bet for Data Scientists looking to maximize their compensation. While the base salary at these shops might not be quite so high, a well-designed equity package can more than make up for the difference when the company makes an exit. It's also a good boost for your portfolio!




For those who graduated around the turn of the last decade, it might have seemed like there were really only two respectable options for starting a high-paying professional career after college: going into finance (specifically, investment banking) or working as a business consultant at a (preferably large and well-known) consulting company. Nowadays, when coding is sexy and data science is white-hot, new graduates have a bit more variety in their options and are given more opportunities to share their stories.

What is more, in many respects, Data Scientists have replaced management consultants thanks to their promise of extracting tangible value and actionable business insights from the large amounts of data available at every turn. Recognizing the value in this trend, large consultancies have started building their own in-house data science groups, such as McKinsey Solutions, Bain Advanced Analytics Group, and BCG Gamma.



2. How Smaller, Younger Companies Offer Higher Pay

When it comes to size, companies with 10,000+ employees tend to offer their Data Scientists the highest salaries according to O’Reilly’s report. However, beware of companies that have been around for more than 10 years! O’Reilly’s Data Scientist salary survey seems to show that these older companies tend to pay their analytics group as much as 15% less on average compared to their younger counterparts.


3. How Company Location Effects Salaries

Just like there is usually no smoke without a fire, the saying that "Data Scientists are data analysts who live in California" has some truth to it. While there are Data Scientists in other parts of the country (and the world), their salaries are typically lower than they would be for the same role in California.

Before you pack your bags and move to San Francisco though, make sure to consider the cost of living in your target city and how far that salary would actually take you. Data Scientist jobs in San Jose and San Diego typically maintain some of the California salary premiums. These cities also provide lower-cost home-based alternatives to San Francisco’s sky-high price tag, according to research done by Springboard.

Alternatively, if you don’t mind the near-constant rain and year-round gloomy weather, Seattle and the surrounding Pacific Northwest could be a great choice of location for your next data science gig. According to Glassdoor, the average Data Scientist salary in Seattle is 5% higher than the national average, while the cost of living in Seattle is significantly lower compared to San Francisco and other cities in California.


4. How Your Role or Title Determines Your Pay

As might be expected, a Data Scientist’s salary will go up as she climbs through the ranks into middle management and beyond. Data Science Managers make as much as $30,000 more on average than an individual contributor, while Directors’ and VPs’ average compensation could be as much as 120% more than a rank and file Data Scientist, according to O’Reilly.

Less predictably though, the different titles of individual contributors doing data science work also correlate with a negative variation in pay. Data science work includes a wide variety of tasks, and not all professionals who do data science work have a Data Scientist title. However, O’Reilly’s most recent salary survey reveals that a Data Scientist, by any other name, is unfortunately paid less than one with the actual title.

Research shows that while Data Analysts or Data Engineers will often do very similar or even the same work as Data Scientists, their salaries are sometimes lower. According to DataJobs.com, a Data Scientist can make as much as 20% more than a Data Engineer and a whopping 40% more than a Data Analyst.



5. How Data Science Tools Help With Higher Pay

When it comes to the tools of the trade, quantity seems to be better than quality. Data Scientists who are familiar with 15 or more tools and technologies have a decided edge over their more specialized peers in terms of salary.

As for those specializing in individual technologies, a positive uptick in salary exists for Data Scientists who use Spark and Scala and, to a lesser extent, for those who are familiar with various tools from the Amazon Web Services suite or similar cloud computing platforms. In terms of visualization technologies, proficiency with d3.js corresponds to higher salaries for data science and analytics professionals.

Here are some books to get you started with these tools:


a book data scientists entitled Advanced Analytics with Spark: Patterns for Learning from Data at Scale  a data analytics handbook entitled AWS Certified Solutions Architect Official Study Guide: Associate Exam a guide for data scientists entitled Data Visualization with Python and JavaScript: Scrape, Clean, Explore & Transform Your Data

Image Source: Amazon



6. How You Negotiate Will Determine Your Outcome

If there is one thing that will make a huge difference in your lifetime earning potential it's your skill as a negotiator. Unlike Python programming, modeling, and stats, negotiation skills are not required for a career in Data Science, but you might find them worthwhile to have regardless if you want to optimize your salary as a Data Scientist.

According to O’Reilly’s Data Science salary guide, people who rated their negotiation skills as excellent could stand to make as much as $50,000 more than their peers who were self-assessed as poor negotiators. What is more, a whopping 18% of people never even attempt to negotiate their salary at all, even though doing so could increase their pay substantially.

If there is one book that you should read if you want to become a better negotiator, it's Jack Chapman's Negotiating Your Salary. Reading this book and doing a few negotiation roleplays with a friend or a mentor before your next salary conversation is almost guaranteed to increase your compensation.

Try it and let me know how it goes!


a guide to competitive salaries entitled Negotiating Your Salary: How to Make $1000 a Minute

Image source: Amazon


Here you have it, folks! Fine-tune these six factors during your data science job search and maximize your Data Scientist salary. 


Claudia Virlanuta

CEO | Data Scientist

Claudia Virlanuta

Claudia is a data scientist, consultant and trainer. She is the CEO of Edlitera, a data science and machine learning training and consulting company helping teams and businesses futureproof themselves and turn their data into profits.

Before Edlitera, Claudia taught Computer Science at Harvard, and worked in biotech (Qiagen), marketing tech (ZoomInfo), and ecommerce (Wayfair). Claudia earned her degree in Economics from Yale, with a focus on Statistics and Computer Science.