Data Scientist Salary: What You Can Realistically Expect to Earn

BLS describes data scientists as people who use analytical tools and techniques to extract meaningful insights from data.^[3] The pay range is wide because the title covers very different work. At the 10th percentile a data scientist earns $67,240; at the 90th, $199,130, a gap of $131,890.^[1] BLS says some data scientists have a coding or engineering background and build machine learning systems, others conduct research for reports or academic journals, and others work on business strategy for marketing, sales, and user engagement.^[3] BLS does not publish pay by specialty, so the federal data cannot say which of those three earns what; what it does show, in the sections below, is that the employer and the state move pay a great deal.

The middle half of the field earns between $85,660 and $158,880. The average, $126,800, sits $6,570 above the median, so a smaller group of high earners pulls the mean up, and the median is the better reference for judging one offer. For context, BLS puts the median for the whole group of mathematical science occupations at $107,570 and for all U.S. occupations at $50,980, which puts the job above its own peer group as well as above the typical American wage.^[3][2]

The federal wage survey counts straight-time gross pay: base salary, cost-of-living allowances, guaranteed pay, and incentive pay such as commissions and production bonuses. It leaves out overtime, severance, nonproduction bonuses, and the employer's cost of benefits.^[4] Any bonus beyond guaranteed pay falls outside the survey, so read the figures here as a guide to base pay and price any bonus or equity separately.

The survey also excludes the self-employed.^[4] That leaves out very few data scientists: BLS estimates 1.9 percent of them are self-employed, against 5.8 percent across all occupations.^[2] Freelance data work exists, but the federal numbers describe a field of employees, and the figures here are a fair picture of it.

Five states hold the largest share of the jobs. California (39,310), Texas (25,860), New York (23,970), Pennsylvania (13,810), and North Carolina (11,430) together account for 43.6 percent of the 262,440 data scientist jobs in the wage survey.^[1] California alone has 15 percent of the national total and pays $141,590, $21,360 above the national median. Texas, the second-largest market, pays $122,090, within $1,860 of the national figure, and New York, the third, pays $130,460.

Washington has the highest median among states with at least 1,000 data scientists, $163,350, which is $21,760 more than California. It is a much smaller market than California, with 9,600 jobs against 39,310. Maryland ($136,370), New Jersey ($135,280), and Massachusetts ($131,750) complete the top five. The pattern is not universal among big markets: Pennsylvania has the 4th-most jobs, 13,810, but a median of $106,850, below the national figure by $13,380. At the bottom of the ranking are Louisiana ($78,760) and Oklahoma ($86,310). Only 13 of the 48 states and DC with a published median sit above the national median, so being in a high-paying state is the exception rather than the rule. BLS does not publish pay by state and industry together, so the table cannot say how much of a state's premium is its employers and how much is its prices; the next section removes the price effect.

The Bureau of Economic Analysis publishes Regional Price Parities, which measure what the same basket of goods and services costs in each state against a national average of 100.^[5] Dividing a state's median by its parity restates the pay in national-average dollars. Among states with at least 1,000 data scientists, these come out on top:

• Washington: $163,350 nominal, about $152,600 after adjusting for prices that run 7.0 percent above the national average.
• Minnesota: $128,800 nominal, about $130,600 after adjusting for prices that run 1.4 percent below the national average.
• Maryland: $136,370 nominal, about $129,900 after adjusting for prices that run 5.0 percent above the national average.
• California: $141,590 nominal, about $127,900 after adjusting for prices that run 10.7 percent above the national average.
• North Carolina: $119,090 nominal, about $126,300 after adjusting for prices that run 5.7 percent below the national average.

Washington leads before and after the adjustment: its $163,350 median becomes about $152,600 even though its prices run 7.0 percent above average. The rest of the order shifts. California is 2nd on nominal pay but 4th after prices, at about $127,900, because its prices run 10.7 percent above average. New York goes from 6th to 12th, to about $120,900, which is close to the national median of $120,230. States with lower prices move up: Texas goes from 12th to 6th at about $125,800, and North Carolina goes from 13th to 5th despite a nominal median of $119,090.

The parities are statewide averages, and the metros where most data science jobs sit usually cost more than the state as a whole. Use the adjusted figures as a first comparison between locations, then check the metro-level parities BEA also publishes before weighing a specific move.

The largest employer is the corporate headquarters. Management of companies and enterprises employs 28,620 data scientists at a median of $128,050, 10.9 percent of the occupation in the wage survey, with computer systems design close behind at 27,590 jobs and $132,380.^[1] BLS's broader grouping shows the same mix, with publishing, broadcasting, and content providers employing 12 percent of data scientists and insurance carriers, computer systems design, and management of companies about 10 percent each.^[3]

Software publishers pay the most among the industries listed here, a median of $156,220 on 10,950 jobs, $35,990 above the national median.^[1] Credit intermediation, mainly banks and lenders, pays $130,300; scientific research and development firms $131,420; and data processing and hosting firms $129,590. Management and technical consulting, with 15,060 jobs, pays $112,520, and insurance carriers, with 15,090 jobs, pay the least of the group at $107,680, still $56,700 above the median for all occupations.^[2]

The $48,540 gap between the highest- and lowest-paying of these industries is larger than the $34,740 gap between California and Pennsylvania. For pay, the kind of company matters as much as the city.

BLS projects employment to rise from 275,600 to 371,000 between 2025 and 2035, 34.6 percent, with about 24,800 openings a year once replacements for people who change careers or retire are counted.^[2] It attributes the growth to rising demand for data-driven decisions and a growing volume of data, and expects firms to keep integrating AI-based systems into their workflows, with data scientists needed to help businesses use AI to improve processes, design products, and market them.^[3]

A projection is a federal estimate, not a hiring forecast for any one employer, and the pay figures above describe people already in the job. What the outlook does tell you is that the occupation is expected to add jobs far faster than the 3.5 percent projected for all occupations, so the field is expanding rather than competing for a fixed number of seats.^[2]

Two job counts appear on this page and measure different things. The wage survey counts 262,440 wage-and-salary jobs in May 2025; the projections count 275,600 jobs in 2025, from a broader set of surveys that includes the self-employed.^[2] Use the first with the pay figures and the second with the growth figures.

Software developers are the best-paid neighbor and the largest: 1,687,890 jobs at a median of $135,980, $15,750 above data scientists.^[1] Computer and information systems managers earn $175,140, the highest median on the list, but BLS lists 5 years or more of related work experience as typical for entry, against none for data scientists, so it is a destination, not a starting point.^[2]

The closer analytical neighbors pay less. Statisticians earn $105,650, $14,580 below data scientists, and BLS lists a master's degree as the typical entry education, while computer systems analysts earn $105,850 and operations research analysts $88,940, both with a bachelor's degree as typical entry education.^[2] Data scientists earn more than all three, and two of the three ask for the same bachelor's degree. Growth differs too: 34.6 percent projected for data scientists against 11.9 percent for operations research analysts, 11 percent for statisticians, and 10.2 percent for software developers.^[2] Our data scientist career guide covers the path in, and our business analyst salary guide covers the business-side alternative.

Check an offer against three figures at once. Start with the state median, not the national one: $120,230 nationally, but $163,350 in Washington and $106,850 in Pennsylvania. Then find the employer's industry in the list above, since the industry spread is wider than most state gaps. Finally, place the offer on the percentile ladder: $85,660 is the 25th percentile and $158,880 the 75th.^[1]

Two adjustments make the comparison fair. Compare base pay with base pay, because the survey leaves out nonproduction bonuses and benefits.^[4] And for a job in another state, use the cost-of-living figures above to compare in the same dollars. A title is a weak guide on its own: BLS says data scientists specialize in engineering, research, or business strategy, and an offer for a research role and an offer for a marketing analytics role can carry the same title.^[3] Ask what the person in the seat builds before comparing the number to a benchmark.

BLS names no certification or license for this occupation, and it does not publish pay by credential, by degree level, or by years of experience.^[3] So no federal figure shows what a certificate or a graduate degree adds to a data scientist's pay.

What the sources do show is how much education the job asks for. BLS says data scientists typically need at least a bachelor's degree, and that some employers require or prefer a master's or doctoral degree.^[3] When O*NET asked workers how much education a new hire needs, 48 percent said a bachelor's degree and 44 percent said a master's degree.^[6] That near-even split is the best evidence that a graduate degree opens more doors without being a gate for everyone. Our master's in business analytics ranking and bachelor's in business analytics ranking list the programs; weigh a degree on its cost and on the employers it opens, not on a pay premium nobody has published.

The largest levers in the federal data are the employer and the state, covered above. Next is the kind of work. BLS describes data scientists who write code and build machine learning systems as one specialty, alongside research and business strategy, and O*NET ranks creating, testing, and validating models among the most important tasks of the job.^[3][6] BLS does not say which specialty pays most, so the employer and state data above are the firmer guide.

Pay by seniority is the one lever the data cannot measure, because BLS does not publish pay by years of experience. The percentile ladder is the guide: moving from the 25th percentile to the 75th is a $73,220 difference, and BLS does not say what separates the top from the middle. The step beyond is management: computer and information systems managers earn a median of $175,140, though BLS lists 5 years or more of related experience as typical for entry.^[1][2] A graduate degree can help, as with an MBA for the management track or a master's in business analytics for the technical one, but weigh it on cost; no federal figure shows its pay effect.