Data scientists are key players in the big data revolution that has been changing the way businesses, government agencies, and non-profits do almost everything. With the analytical and quantitative skills required to unravel the mysteries hidden in big data, data scientists deliver the fuel required to drive organizational goals and offer surprising insights about the behavior of people and systems that before remained obscure and ignored.
So it should come as no surprise that working with big data can offer up big paychecks. Companies may be willing to pay an arm and a leg to a talented data scientists, but it’s only because the value they bring to the table offers even bigger profits. McKinsey Global Institute’s December 2016 analysis of how big data is impacting business brings to light some major disparities emerging between industry leading enterprises and average companies, a dynamic that is creating a winner-take-all scenario in some market verticals.
When it’s a case of ratcheting-up salaries for data scientists or risking irrelevance, you can be sure companies are going to spend what it takes to stay competitive.
In recent years, data storage prices have plummeted, allowing many organizations to save and catalog enormous sets of unstructured data. This has resulted not only in increased job opportunities for data scientists but increased salaries, as well. The longer valuable data sits there, unexplored, un-mined, the more of the benefits of collecting that data are squandered. Companies are willing to pay a premium to get accomplished and experienced data scientists on the job ASAP.
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Wanted: Data Scientists (And Willing to Pay)
A 2017 study by IBM and job market data firm Burning Glass Technologies projects a 39% increase in demand for data scientists and data engineers by 2020. That’s a total of 2.7 million jobs, which McKinsey estimates will be around 250,000 more openings than there are qualified candidates to fill them.
Increasing requirements for specializations further narrows the window for top talent in a way that these reports can’t fully quantify. The risk for firms, and job seekers, is that the skills required for a particular position might not be met by candidates even if technically, there are enough data scientists available.
This has lead to a market that has not only boosted salaries for individuals, but has driven up the valuation of entire technology firms heavy in big data talent. In 2014, Google acquired artificial intelligence firm DeepMind for more than $600 million, a price that breaks down to almost $7 million per employee… many of them data scientists who simply couldn’t be hired any other way.
But companies are certainly making an effort to attract top talent with big paychecks. O’Reilly’s 2016 Data Science Salary Survey found that U.S.-based data scientists enjoyed a median salary of $106,000. Among respondents, the real story wasn’t simply the current base salary, though; for around half of them, the number had jumped by 20% year-over-year, and for a lucky 12%, their salary had at least doubled.
Pay Hikes are Going to Leaders and Specialists in the Field
In their 2017 salary survey, Burtch Works, an executive talent recruiting firm, breaks the numbers down even further, classifying data scientists according to three different career levels and based on whether or not they are in a management role:
|Position||Median Salary||Percentage Increase YoY|
|Entry-level Data Scientist||$95,000||-2%|
|Mid-career Data Scientist||$126,000||1%|
|Advanced Data Scientist||$150,000||3%|
Not only is the range surprising, but the fact that entry-level positions dropped slightly reflects the increasing demand for specialization… a skillset that isn’t available yet to most newly minted data scientists. The demand for managerial talent at the entry level, however, echoes a finding in the McKinsey report that some 45% of organizations report their biggest obstacle to leveraging big data is in “…designing an appropriate organizational structure to support data and analytics activities.”
McKinsey also finds that the issue is more prominent in some industries than others, with manufacturing, public sector, and retail organizations lagging in the adoption and leveraging of data science tools.
Another study by McKinsey projects that by 2018, the U.S. may face a 50 to 60 percent gap between supply and demand of “deep analytic talent.” There are a number of industries already experiencing this shortage, including insurance, finance, pharmaceuticals, and aerospace.
Salaries by City and Region: Where They Are, Where They’re Headed
The Robert Half Technology 2017 Salary Guide revealed a 6.4% increase in data scientist salaries between 2016 and 2017… less than the 9% seen the year before, but still the single largest bump among all job categories the company tracks. According to Robert Half, the salary range for data scientists in 2017 was $116,000 – $163,500.
Salary ranges for data scientists in some of the country’s largest cities (as of 2016) include:
- New York, NY: $152,600 – $215,250
- Dallas, TX: $118,265 – $166,819
- Los Angeles, CA: $139,520 – $196,800
- Miami, FL: $116,630 – $164,512
- Seattle, WA: $129,601 – $182,809
- Pittsburgh, PA: $106,820 – $150,675
- Saint Louis, MO: $109,000 – $153,750
- Atlanta, GA: $115,119 – $162,382
- Chicago, IL: $134,070 – $189,112
The 2017 Burtch Works publication “Salaries of Data Scientists” broke down salaries according to geographical region. For example, in every job category, data scientists earned more on the West Coast and the Northeast than those in the Middle U.S. West Coast data scientists posted the highest salaries in the nation, bringing home a median base salary at mid-career of $140,000; that’s 8% more than their counterparts in the Northeast and 14% more than those in the Middle U.S.
The Burtch Works study broke down data scientist salaries further by seniority/position, with Level 1 being entry-level positions and Level 3 being senior positions:
- Level 1
- Northeast: $90,000
- Middle U.S.: $88,500
- West Coast: $102,500
- Level 2
- Northeast: $130,000
- Middle U.S.: $120,000
- West Coast: $140,000
- Level 3
- Northeast: $160,000
- Middle U.S.: $155,000
- West Coast: $167,500
This is leveling out, however… the major increases in salary from the pervious year’s numbers happened primarily in the Northeast and Midwest, with the Midwest experiencing the highest jump, while West Coast data scientists actually saw median salary increases correct and pull back a bit.
Burtch Works also revealed annual, average salaries for data scientist managers, by region and by level:
- Level 1
- Northeast: $150,000
- Middle U.S.: $140,000
- West Coast: $153,800
- Level 2
- Northeast: $191,500
- Middle U.S.: $180,000
- West Coast: $200,000
- Level 3
- Northeast: $240,000
- Middle U.S.: $250,000
- West Coast: $275,000
Beyond Salary: Perks and Advantages of Being a Data Scientist
Salaries have been fluctuating somewhat unpredictably as organizations identify and look to fill specialty roles, and in response to the influx of new job-seekers entering the field. To some extent, the field of data science is sill defining itself and its role within certain industries. After all, the field was first identified and classified just ten years ago in 2008.
Since data science is all about discovering new ways to leverage the massive amount of data organizations have been accumulating, it isn’t completely clear yet in all verticals exactly what the next discovery will yield and how it will be applied. To some degree, the role remains open to interpretation among analysts and hiring managers alike. In some instances, the true value of a data scientist may not be fully understood until they are actually on the job.
For that reason, starting salaries are not the only measure of demand, and they shouldn’t be the only factor you consider in your job search.
Burtch Works, for example, notes that 85% of survey respondents are eligible for bonuses over and above the base salary. Median bonus numbers were reported to be from $10,000 to $29,500.
There is also much value to be found in having a job that allows for mobility. O’Reilly found that 64% of data scientists found it easy or very easy to locate a new position. That provides enormous bargaining power when it comes to negotiating a salary, not to mention real quality-of-life benefits when it comes to choosing where and how you want to live.
A solid education is a must-have for a prospective data scientist and an advanced degree is a big investment. But with salaries in the field booming, its an investment that will pay big dividends for years to come.