Data Scientist Salary in 2026: National, by State, City, Specialty & Real Value

A data scientist in the United States earns a median of $120,230 a year, about $57.80 an hour, according to the U.S. Bureau of Labor Statistics (2025). Pay runs from roughly $67,240 at the 10th percentile to $199,130 at the 90th, and it swings further once you account for state, metro, setting, specialty, cost of living, and tax. About 262,440 data scientists work nationwide. This guide is the complete picture: national and percentile pay, all 50 states ranked by real (cost-adjusted) value, top metros, settings, specialties, take-home after tax, the path in, the return on the degree, and answers to the questions people ask most.

National pay overview

The national median for data scientists is $120,230 a year ($57.80 an hour), with a mean of $126,800. The full wage curve, from entry to senior:

Percentile	Annual	Hourly
10th (entry)	$67,240	$32.33
25th	$85,660	$41.18
50th (median)	$120,230	$57.80
75th	$158,880	$76.38
90th (senior)	$199,130	$95.74

The gap from the 10th to the 90th percentile is about $131,890. A single national number hides that range, which is why the rest of this guide breaks pay down by every factor that moves it, and then translates the headline into cost-adjusted, after-tax dollars.

The mean of $126,800 sits above the median, which tells you the top of the field pulls the average up: a meaningful share of data scientists earn well into the upper percentiles through specialty, setting, geography, and seniority. The practical question is what you can earn given where you work, what you specialize in, and how you structure your hours, which matters far more than the field-wide average. Each section below answers one piece of that.

Pay by experience

BLS does not publish pay by years of experience, but the percentiles map closely to a career arc:

Stage	Typical percentile	Annual
New graduate	10th to 25th	$67,240 to $85,660
Mid-career	~50th	around $120,230
Experienced	~75th	around $158,880
Senior / specialized / lead	90th+	$199,130 and up

The climb from new graduate to senior is roughly $131,890. Early raises tend to come fastest as you move off new-grad pay; by mid-career the curve flattens, and the people who keep climbing usually do so by specializing, switching to a higher-paying setting or state, taking on lead and management duties, or adding hours through overtime and extra work. Geography and setting can outweigh experience entirely: an experienced data scientist in a low-paying state can earn less than a new graduate in a top-paying one.

Salary by state: all 50 states ranked by real value

This is the table most data scientist salary pages leave out. Below are every state and Washington, D.C., ranked by real value (the median adjusted for that state’s cost of living (BEA Regional Price Parities, 2024)) rather than by sticker pay. A high-paying state with high costs can leave you worse off than a moderate-paying, low-cost one. Tap a linked state for the full local breakdown.

#	State	Median (nominal)	Cost level (US=100)	Real value	Employed
1	Washington	$163,350	107.0	$152,645	9,600
2	Minnesota	$128,800	98.6	$130,601	4,020
3	Maryland	$136,370	105.0	$129,927	3,340
4	Vermont	$127,070	98.0	$129,719	200
5	California	$141,590	110.7	$127,881	39,310
6	North Carolina	$119,090	94.3	$126,254	11,430
7	Arkansas	$109,390	86.9	$125,827	530
8	Texas	$122,090	97.1	$125,792	25,860
9	Virginia	$126,430	101.1	$125,049	8,920
10	Massachusetts	$131,750	105.8	$124,578	9,420
11	New Jersey	$135,280	108.8	$124,333	6,430
12	Connecticut	$126,340	103.6	$121,938	1,760
13	Oregon	$125,990	103.4	$121,893	2,140
14	New York	$130,460	107.9	$120,885	23,970
15	Alabama	$103,120	88.8	$116,096	1,740
16	District of Columbia	$126,490	109.9	$115,094	2,680
17	Colorado	$117,400	103.1	$113,923	6,280
18	Wisconsin	$106,680	94.1	$113,375	4,370
19	Florida	$115,820	103.4	$111,996	10,240
20	Ohio	$102,630	92.8	$110,624	6,100
21	Pennsylvania	$106,850	97.6	$109,509	13,810
22	Utah	$108,090	98.9	$109,332	4,360
23	Tennessee	$100,330	91.9	$109,209	2,790
24	Iowa	$95,830	87.8	$109,193	2,300
25	Nebraska	$98,230	90.1	$109,020	1,980
26	Kansas	$98,130	90.1	$108,951	490
27	South Dakota	$96,150	88.6	$108,539	220
28	Georgia	$104,340	96.3	$108,357	9,260
29	Missouri	$97,090	90.8	$106,907	4,730
30	Illinois	$106,560	100.0	$106,605	10,520
31	Arizona	$107,240	100.7	$106,519	4,560
32	Montana	$100,490	94.6	$106,176	220
33	Michigan	$100,590	96.2	$104,545	7,360
34	New Mexico	$95,850	92.2	$103,945	560
35	Kentucky	$91,760	90.2	$101,776	1,540
36	Rhode Island	$102,440	102.3	$100,156	1,160
37	Nevada	$98,540	100.0	$98,561	1,680
38	Oklahoma	$86,310	87.8	$98,255	1,900
39	South Carolina	$91,990	93.7	$98,124	3,750
40	Indiana	$91,470	93.3	$98,008	3,580
41	New Hampshire	$100,620	104.2	$96,597	1,100
42	West Virginia	$85,090	89.5	$95,076	390
43	Maine	$90,880	97.0	$93,642	1,210
44	Idaho	$89,380	95.5	$93,598	1,350
45	Hawaii	$102,130	110.0	$92,887	300
46	North Dakota	$81,900	89.0	$92,065	210
47	Louisiana	$78,760	88.2	$89,290	1,300
48	Alaska	$84,110	102.4	$82,172	230
49	Mississippi	$69,490	87.0	$79,917	440

On real, cost-adjusted value, Washington leads at $152,645, while Mississippi trails at $79,917. By raw sticker pay the order is different: Washington ($163,350), California ($141,590), and Maryland ($136,370) pay the most nominally, but several of them slide down the list once high housing and prices are counted. That reordering is the single most useful thing this page does, and it is why national averages and sticker rankings can steer you wrong.

Concrete example of the flip: Hawaii ranks #27 by sticker pay ($102,130) but only #45 once its cost level of 110 is applied, because high prices eat the higher salary. Meanwhile Iowa looks middling on sticker (#38) yet climbs to #24 on real value, since a 95,830-dollar median goes much further at a cost level of 88. If you are willing to relocate, the real-value column, not the sticker column, is the one that should guide the decision.

The geographic pattern: the highest sticker pay clusters on the West Coast and in the Northeast, while the best real value often shows up in lower-cost states in the South, Midwest, and Mountain West where a strong salary meets cheap housing. There is no single best state, only the best fit for where you want to live and what your money will buy there.

Highest-paying metros

Within states, metros drive pay further. The largest data scientist job markets by employment, with median pay:

Metro	Median	Employed
New York-Newark-Jersey City, NY	$135,980	23,160
San Francisco-Oakland-Fremont, CA	$170,110	10,460
Dallas-Fort Worth-Arlington, TX	$127,750	10,120
Los Angeles-Long Beach-Anaheim, CA	$129,740	9,850
Washington-Arlington-Alexandria, DC	$132,200	9,260
Seattle-Tacoma-Bellevue, WA	$164,740	8,370
Chicago-Naperville-Elgin, IL	$107,640	7,940
Boston-Cambridge-Newton, MA	$132,040	7,930
Atlanta-Sandy Springs-Roswell, GA	$108,940	6,820
Philadelphia-Camden-Wilmington, PA	$109,910	6,480

The same cost-of-living rule applies inside a state: a higher-paying big metro can lose to a cheaper mid-size city once housing is counted. Big metros also hold the deepest job markets, so they pair the most pay with the most openings, while rural and smaller markets sometimes pay premiums to attract candidates. The state pages work the metro and cost math out city by city.

Salary by work setting

Where data scientists work changes pay as much as geography. National medians by employer type:

Setting	Employed (U.S.)	Median
Management of Companies and Enterprises	28,620	$128,050
Computer Systems Design and Related Services	27,590	$132,380
Insurance Carriers	15,090	$107,680
Management, Scientific, and Technical Consulting Services	15,060	$112,520
Credit Intermediation and Related Activities (5221 and 5223 only)	12,450	$130,300
Scientific Research and Development Services	11,480	$131,420
Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services	11,320	$129,590

The gap between the highest- and lowest-paying settings is real money over a career, and it usually comes with trade-offs in pace, caseload, autonomy, and schedule rather than in difficulty alone. The largest employer is not always the best payer, so it is worth weighing where the volume of jobs is against where the pay is when you choose a setting.

How it compares to related careers

It helps to see a data scientist beside the roles people weigh against it, with pay set next to the education each requires:

Role	National median	Education
Computer Systems Analyst	$105,850	Bachelor’s (varies)
Data Scientist (this role)	$120,230	Bachelor’s/master’s
Software Developer	$135,980	Bachelor’s or self-taught
Information Systems Manager	$175,140	Bachelor’s + experience

Against computer systems analyst at $105,850, this role pays about $14,380 more for the added schooling. The higher-paid software developer ($135,980) sits $15,750 above, but on a longer or different training path. The right comparison is always pay set against the time, cost, and debt of the credential, not pay alone.

Skills, specialization, and what they mean for pay

There is no license in data science; skills are everything. Pay is gated by your toolkit (Python, SQL, machine learning, statistics, cloud), the depth of specialization (ML engineering, NLP, computer vision), and the employer tier, far more than the degree, though many roles still expect a quantitative bachelor’s or master’s. Those who move from analysis into machine-learning engineering and production systems command the most.

Licensing is not just a hurdle, it shapes pay. Where a role can practice more independently or bill for more services, it tends to command more, and license portability between states affects how easily you can chase a higher-paying market. Always confirm the current rules with the relevant state board, since scope and requirements change and vary widely.

Specialties and where the pay is

Specialization drives huge pay gaps. Machine-learning engineers, AI and LLM specialists, and data scientists at large public tech companies earn well above the median once equity is counted, while generalist analytics roles pay less. Moving from analysis into ML engineering, research, or data-science management raises the ceiling sharply.

The practical takeaway: within data scientists, specialty and setting usually move pay more than another year of general experience. The top earners are rarely just the most tenured, they are the ones in the higher-paying focus areas, settings, or leadership and ownership roles.

If you are early in the field, the decisions with the biggest long-run payoff are which specialty to pursue and which setting to enter, because both compound over a career and are easier to choose early than to switch later. A credential that takes a year to earn can pay for itself many times over through higher pay and more job options, which is the same logic the ROI section applies to the degree itself.

What moves the pay

• State and metro, and crucially the cost of living that goes with them, which the real-value table reorders.
• Experience, which lifts pay steadily and then plateaus without a specialty or a step up.
• Work setting and employer, since some settings and employers pay well above others, as the settings table shows.
• Specialty and board certification, a clear premium in most of these fields.
• Hours and structure, since overtime, extra shifts, and contract or travel work can push total pay well above base.

Salary, hourly, and total compensation

The $120,230 median is base pay, and real total compensation often runs higher. Base salary understates total pay for many data scientists. At larger and public tech and finance employers, equity or stock (RSUs) and bonuses can add a large share on top of base, so total compensation can run well above the BLS median, which captures base wages. The equity component is a big reason data-science pay varies so widely. When comparing offers, weigh the whole package, base, any bonus or equity, overtime or premiums where they apply, retirement match, and paid time off, since two offers with the same base can differ by thousands once the rest is counted.

Take-home pay after tax

Two data scientists on the same $120,230 salary keep very different amounts depending on the state. Worked examples on the national median, single filer, 2026 federal plus FICA plus state:

State	Gross	Est. take-home	Effective rate
Texas (no state income tax)	$120,230	$92,930	22.7%
New York	$120,230	$86,765	27.8%
California	$120,230	$85,722	28.7%

That is roughly $7,208 a year more in take-home in no-tax Texas than in California on an identical salary, before cost of living is even counted. Nine states levy no income tax.

Take-home also scales with where you sit on the pay curve. In Texas, an entry-level data scientist earning $85,660 nets about $68,648, while a senior one at $199,130 keeps about $148,286, since higher pay pushes more income into higher federal brackets. The effective rate climbs with income, so a raise is worth somewhat less on take-home than on the headline. Every state page includes a full breakdown and a paycheck calculator to run your own number.

How to maximize earnings

• Target a high-real-value state or metro using the table above, not the highest sticker.
• Move into the higher-paying setting and specialty for your field.
• Add board certification or a specialty credential, which pays a clear premium.
• Use overtime, contract, or travel work to lift total pay, and negotiate the full package.
• Consider leadership or ownership for the higher ceiling.

How pay is changing

Wages for data scientists have broadly risen with demand and inflation, but the real story is in the mix: pay grows fastest where labor is scarce and where the role takes on more responsibility. Watch three things if you are planning a career here, the spread between settings (which keeps widening as specialized and higher-skill roles pull ahead), the value of cost-of-living arbitrage (a strong salary in a cheap state has rarely been worth more relative to expensive coastal markets), and the premium on specialty credentials. The figures on this page are the May 2025 BLS estimates, the most recent national data, and the state pages carry the same detail locally.

Job outlook

Employment of data scientists is projected to grow about 34% from 2024 to 2034, much faster than the average for all occupations and the fourth-fastest growth of any occupation in the economy, with roughly 23,400 openings a year, according to the U.S. Bureau of Labor Statistics. The explosion of data and AI-driven decision-making is the engine: organizations need people who can turn large, messy datasets into decisions, and the skill set remains hard to hire for.

For context, the average growth rate across all U.S. occupations through 2034 is about 3%. A large share of yearly openings also comes from replacing workers who retire or move on, so real hiring tends to run ahead of the net-growth figure, and shortage and rural areas often pay the most to attract candidates.

How to become a data scientist

Data science rewards demonstrable quantitative skill, with very strong demand and a high ceiling:

1. Build a quantitative foundation through a bachelor’s or master’s in a field like statistics, computer science, math, or economics (some enter through demonstrable skills).
2. Learn the core toolkit of Python, SQL, statistics, and machine learning, with a portfolio of real projects.
3. Land a first analytics or data role and build experience with production data.
4. Specialize into ML engineering, AI, or a high-paying domain, and target higher-tier employers for the equity upside.

Is it worth it? The return on the degree

The return is among the strongest of any field: a quantitative degree or demonstrable skills lead to a median near $120,230, with a far higher ceiling at top employers once equity is counted, and demand is the fourth-fastest in the economy. The investment is less about a specific credential than about building real skills and a track record. Weigh program cost, your starting state and setting, and the specialties you can reach against the debt. The pay tables above, set next to the education column in the comparison section, are the honest way to run that math before committing.

Two levers change the answer most: the price of the program you choose, since cost varies enormously between public and private schools, and the state and setting you start in, since the same degree pays very differently across the by-state and settings tables above. A graduate who controls program cost and starts in a high-real-value state can clear the debt years faster than one who does neither, on the identical credential.

Frequently asked questions

State guides and tools

Sources: U.S. Bureau of Labor Statistics, OEWS May 2025 and Occupational Outlook Handbook (SOC 15-2051 and related codes); national, state, and metro estimates. U.S. Bureau of Economic Analysis, Regional Price Parities, 2024. Take-home figures are 2026 estimates (federal, FICA, and state) for a single filer and will vary with deductions and filing status. See our methodology.