A percentile is a position in the BLS distribution. It is not a promised offer or career forecast.
10th
$67,240
25th
$85,660
Median
$120,230
75th
$158,880
90th
$199,130
National context
What changes around the median
Data Scientists ranked 8 of 21 detailed computer and mathematical occupations with a published annual median. The national annual mean was 5.5% above the median, showing why one center point does not describe the full distribution.
Area estimates below use the same SOC code and release. They do not adjust for cost of living, experience, benefits, hours, employer mix, or remote-work policy.
Published metro rows
Highest medians for Data Scientists
Ranked only among metros with a published annual median. No cost-of-living adjustment.
Six highest published annual medians
San Jose-Sunnyvale-Santa Clara, CA: $185,080. San Francisco-Oakland-Fremont, CA: $170,110. Idaho Falls, ID: $167,840. Seattle-Tacoma-Bellevue, WA: $164,740. Kennewick-Richland, WA: $156,830. Charlottesville, VA: $149,800.
Use the published range, job count, place pattern, and survey limits as separate facts. The guide stays tied to SOC 15-2051 and the May 2025 BLS release.
Questions, answered
Data Scientists: common wage questions
What is the median wage for Data Scientists?
The May 2025 BLS OEWS release published a U.S. median of $120,230 per year for Data Scientists. It is an occupation estimate, not a promised offer or an individual's pay.
How many Data Scientists jobs does BLS estimate nationally?
BLS published an employment estimate of 262,440 wage and salary jobs. The estimate is not a count of current openings and excludes the self-employed.
Why is the mean wage different from the median for Data Scientists?
The published annual mean is 5.5% above the median. The mean uses every published wage value, while the median marks the middle of the distribution.
Which metro has the highest published median for Data Scientists?
San Jose-Sunnyvale-Santa Clara, CA has the highest annual median among the metro rows with a published value on this page, at $185,080. This is a nominal BLS estimate with no price-level adjustment.
From market data to your evidence
Tailor for a real Data Scientists job, not a generic wage number.
Pick an eligible listing, then MeritLog can help you prepare from experience you have confirmed. WageScope never turns a BLS estimate into a qualification or résumé claim.
The May 2025 BLS OEWS estimate puts the U.S. median for Data Scientists at $120,230 per year. Median means the middle. Half of the wage values used for this estimate fall below that point. Half fall above it. The median is not one worker's pay. It is not a job offer. It is one clear mark for the middle of a broad national pay range.
BLS also lists 262,440 wage and salary jobs for Data Scientists. That count covers jobs in the survey's scope. It does not count open job ads. It also leaves out people who work for themselves. The wage and job counts share the same May 2025 reference point, so they can be read together without mixing time periods.
The Data Scientists mean of $126,800 per year is 5.5% above the $120,230 median on the same published median. Mean means BLS adds the wage values and divides the total by the number of jobs behind the estimate. A gap between mean and median shows that the shape is not even. It does not explain the cause of the gap.
BLS names this record Data Scientists and gives it SOC 15-2051. The two-part code is the stable key for this page. A role with a close name keeps a different code, wage row, job count, and area pattern. WageScope never joins two SOC records because their words sound alike.
For SOC 15-2051, the U.S. row reports 262,440 wage and salary jobs. Its annual median is $120,230 per year. Its matching mean is $126,800 per year. These facts come from one national row and one May 2025 release.
The same SOC 15-2051 appears in 51 state or territory rows, 291 metro rows, and 82 nonmetro rows in this artifact. A missing local row stays missing. The national record does not fill it, and a nearby SOC does not stand in for it.
Read the Data Scientists wage ladder from low to high
The 10th percentile is $67,240 per year. The 25th percentile is $85,660 per year. The median is $120,230 per year. The 75th percentile is $158,880 per year. The 90th percentile is $199,130 per year. Each point marks a place in the same national wage spread.
A percentile is a rank point. It is not a career step. For example, the 10th percentile means 10% of wage values are at or below that mark. It does not mean a new worker starts there. The 90th percentile means 90% are at or below that mark. It does not mean a worker can reach it after a set number of years.
The middle half runs from $85,660 to $158,880 per year. That span is $73,220. The full 10th-to-90th span is $131,890. These spans show why one median cannot stand in for every Data Scientists job.
What the gaps around the Data Scientists median show
The median is 78.8% above the 10th percentile. The 90th percentile is 65.6% above the median. The two sides do not have to match. A wider upper side can pull the mean up. A wider lower side can pull it down. The data shows the shape, but not the reason for that shape.
The 25th-to-median step is $34,570 per year. The median-to-75th step is $38,650 per year. These two steps give a closer look at the middle half. They are often more useful than a single top-to-bottom gap because they keep the center in view.
Use this ladder as a fact check when you read a job post. A posted range can sit below, near, or above these BLS marks. That does not make the post wrong. A job may differ by place, hours, level, field, employer, or work rules. Keep the posted range tied to that listing. Keep the BLS range tied to the whole occupation.
Metro areas with the highest published Data Scientists medians
WageScope found 282 metro rows with the same published annual median for Data Scientists. It sorts only those rows. It does not fill a blank row. It does not turn a blank into zero. The first six rows below have the highest published medians in this release. The list is a wage rank, not a rank of places to live or work.
The first row is San Jose-Sunnyvale-Santa Clara, CA, at $185,080 per year. The middle metro row by published pay is Duluth, MN-WI, at $99,400 per year. That middle metro mark gives one more check on how far the top rows sit from a common place in the set.
These are nominal wages. Nominal means the dollar amount shown in the source. This list does not adjust for local price levels. It also does not tell us how many openings an area has now. Use a local page to see price-level context when an exact BEA row exists. Use the separate job block to inspect current employer-title matches.
1. San Jose-Sunnyvale-Santa Clara, CA has a Data Scientists median of $185,080 per year, or $64,850 above the U.S. Data Scientists median. BLS lists 6,060 jobs there, against 262,440 across the U.S. The local location quotient is 3.16.
2. San Francisco-Oakland-Fremont, CA has a Data Scientists median of $170,110 per year, or $49,880 above the U.S. Data Scientists median. BLS lists 10,460 jobs there, against 262,440 across the U.S. The local location quotient is 2.61.
Job count
Metro areas with the largest Data Scientists job estimates
A large wage can appear in a small job market. A large job market can have a lower wage. That is why WageScope keeps pay and job count in separate views. The six rows below have the largest published employment estimates for Data Scientists among metro rows in this release. Employment means the number of wage and salary jobs in scope. It does not mean open roles.
New York-Newark-Jersey City, NY-NJ has the largest listed metro estimate in this set, at 23,160 jobs. Its median is $135,980 per year. Compare that with San Jose-Sunnyvale-Santa Clara, CA, which leads the published median list. The leaders may match, or they may not. Each list answers a different question.
Job count can help you see the scale of a local labor market. It cannot show churn, new hiring, job quality, remote work, or the chance that one person will get hired. Current listings add a second view, but they are a dated sample of posts. WageScope never turns the listing count into a demand score.
1. New York-Newark-Jersey City, NY-NJ has 23,160 estimated Data Scientists jobs, or 8.8% of the national 262,440 job estimate. Its median is $135,980 per year, or $15,750 above the U.S. Data Scientists median. Its location quotient is 1.45.
Local share
Where Data Scientists has a larger share of local jobs
Location quotient is a share check. A value of 1 means the role has the same share of local jobs as it has across the nation. A value above 1 means the local share is larger. A value below 1 means the local share is smaller. It is not a count. It is not a wage. It does not say that a place is growing.
Bloomington, IL has the highest published location quotient in this bounded metro set, at 4. The same row lists 590 jobs and a median of $120,760 per year. Reading all three values keeps a high share from being mistaken for a large market or high pay.
A small area can have a high location quotient because the role makes up a large part of a small local job base. A large area can have many workers and a lower quotient. Use the quotient to ask where the role is common in the mix. Use employment to ask how many jobs BLS estimated. Use the wage fields to ask what the published pay range looks like.
1. Bloomington, IL has a Data Scientists location quotient of 4. BLS lists 590 jobs and a median of $120,760 per year, or $530 above the U.S. Data Scientists median. The U.S. job estimate is 262,440.
2. San Jose-Sunnyvale-Santa Clara, CA has a Data Scientists location quotient of 3.16. BLS lists 6,060 jobs and a median of $185,080 per year, or $64,850 above the U.S. Data Scientists median. The U.S. job estimate is 262,440.
SOC family
How Data Scientists sits in Computer and Mathematical
Data Scientists is SOC 15-2051. BLS places it in the Computer and Mathematical major group. Among 21 detailed roles in that group with a published annual median, Data Scientists ranks 8 of 21 by median. This is a pay rank inside one source family. It is not a rank of skill, value, status, or career fit.
The closest same-family medians help set a fair peer frame. They do not claim that the work is the same. Official SOC titles can cover different tasks and settings. WageScope uses the family only because BLS publishes that link. It does not infer a career move, a skill match, or a hiring path from the wage gap.
Use these peer rows to spot whether the $120,230 median sits near many family roles or far from them. Then open a role's own page before using its number. Each role has a different code, job count, wage spread, and place pattern. One family label does not erase those facts.
1. Computer Occupations, All Other, SOC 15-1299, has a U.S. median of $116,580 per year. Its gap from Data Scientists is $3,650 per year.
2. Mathematicians, SOC 15-2021, has a U.S. median of $126,710 per year. Its gap from Data Scientists is $6,480 per year.
3. Information Security Analysts, SOC 15-1212, has a U.S. median of $129,180 per year. Its gap from Data Scientists is $8,950 per year.
Data care
How much care to use with the Data Scientists estimate
BLS marks the employment relative standard error at 1.1%. It marks the mean-wage relative standard error at 0.7%. A relative standard error, or RSE, is a survey precision flag. A smaller RSE often means less sampling uncertainty. It is not the chance that one worker's pay is right or wrong.
The employment RSE applies to the 262,440 job estimate. The mean-wage RSE applies to the published mean of $126,800 per year. These fields do not give a matching RSE for the median or each percentile. WageScope shows the fields BLS published and does not move one RSE to another measure.
Survey values can change when BLS updates its methods, sample, models, or source year. That is why this page keeps May 2025 in view. A later release can replace these facts only after MeritLog pins the new official files, checks the rows, and reviews the page again. A changed number is not treated as a correction unless the source says so.
A simple way to compare Data Scientists across places
First, compare the same wage metric. Use annual with annual or hourly with hourly. Next, check the source date. Keep May 2025 BLS rows together. Then read employment and location quotient beside pay. This stops a high median from standing in for market size or local share. Last, add BEA price-level context only when the exact place has an approved row.
A state, metro, and nonmetro area are not the same kind of map. Do not rank them in one list without saying so. WageScope groups like with like. It also keeps a national row as a broad bench mark. A local page can show the local row, its state row when that link is exact, and the U.S. row without blending them.
If a value is blank, keep it blank. If BLS marks a top code, read it as a lower bound. Do not fill a blank from a nearby place. Do not turn a wage gap into a claim about taxes, hours, benefits, work rules, or a household budget. Those facts are not in the OEWS row.
Move from Data Scientists wage data to one real listing
The job block on this page uses a strict employer-title match for Data Scientists. It may show fewer rows than the full MeritLog job search. That is on purpose. A false negative is safer here than a job card for a different role. Each card keeps its employer, place, source, and seen date. The cards do not change the BLS wage data.
Open one listing and read its own pay, work place, level, and needs. Compare its posted range with the BLS spread only as context. A posted range is set by an employer for one role. The BLS range is a survey view across many jobs. Neither source proves that a person is fit for the work.
When you tailor a résumé, use work facts you can back up. Do not turn $120,230 into a skill or result. Do not copy a BLS job count into your work history. MeritLog can help shape confirmed experience for a chosen listing. The wage page stays public and read only. It does not save a job or create a résumé on page load.
This page does not say what one Data Scientists worker earns. It does not promise that a job pays the median. It does not state a starting wage. It does not explain why the mean and median differ. It does not add benefits, tips, stock, bonus pay, taxes, or work hours unless BLS includes them in the published wage measure.
The page does not count people who work for themselves. It does not turn 262,440 into current openings. It does not read private MeritLog jobs or Career Memory. MeritLog does not use the page to build a private career profile. While public analytics are on, GA4, Ahrefs, and Meta may receive the public page URL; Your Privacy Choices or Global Privacy Control turns that off. The page does not send its text to a model.
The best use is narrow and clear. Read the May 2025 BLS wage range. Check how the role changes by place. Add the separate 2024 BEA price level when an exact row exists. Inspect a dated employer-title match. Then make a résumé choice from facts you have confirmed. Keep each source tied to the question it can answer.
Are the current job listings included in the BLS wage calculation?
No. MeritLog shows eligible live public-catalog listings separately, with their own source and observation date. Posted pay never changes the official BLS distribution.
3. Idaho Falls, ID has a Data Scientists median of $167,840 per year, or $47,610 above the U.S. Data Scientists median. BLS lists 230 jobs there, against 262,440 across the U.S. The local location quotient is 1.68.
4. Seattle-Tacoma-Bellevue, WA has a Data Scientists median of $164,740 per year, or $44,510 above the U.S. Data Scientists median. BLS lists 8,370 jobs there, against 262,440 across the U.S. The local location quotient is 2.38.
5. Kennewick-Richland, WA has a Data Scientists median of $156,830 per year, or $36,600 above the U.S. Data Scientists median. BLS lists 150 jobs there, against 262,440 across the U.S. The local location quotient is 0.68.
6. Charlottesville, VA has a Data Scientists median of $149,800 per year, or $29,570 above the U.S. Data Scientists median. BLS lists Not published jobs there, against 262,440 across the U.S. The local location quotient is Not published.
2. San Francisco-Oakland-Fremont, CA has 10,460 estimated Data Scientists jobs, or 4.0% of the national 262,440 job estimate. Its median is $170,110 per year, or $49,880 above the U.S. Data Scientists median. Its location quotient is 2.61.
3. Dallas-Fort Worth-Arlington, TX has 10,120 estimated Data Scientists jobs, or 3.9% of the national 262,440 job estimate. Its median is $127,750 per year, or $7,520 above the U.S. Data Scientists median. Its location quotient is 1.48.
4. Los Angeles-Long Beach-Anaheim, CA has 9,850 estimated Data Scientists jobs, or 3.8% of the national 262,440 job estimate. Its median is $129,740 per year, or $9,510 above the U.S. Data Scientists median. Its location quotient is 0.93.
5. Washington-Arlington-Alexandria, DC-VA-MD-WV has 9,260 estimated Data Scientists jobs, or 3.5% of the national 262,440 job estimate. Its median is $132,200 per year, or $11,970 above the U.S. Data Scientists median. Its location quotient is 1.75.
6. Seattle-Tacoma-Bellevue, WA has 8,370 estimated Data Scientists jobs, or 3.2% of the national 262,440 job estimate. Its median is $164,740 per year, or $44,510 above the U.S. Data Scientists median. Its location quotient is 2.38.
3. San Francisco-Oakland-Fremont, CA has a Data Scientists location quotient of 2.61. BLS lists 10,460 jobs and a median of $170,110 per year, or $49,880 above the U.S. Data Scientists median. The U.S. job estimate is 262,440.
4. Seattle-Tacoma-Bellevue, WA has a Data Scientists location quotient of 2.38. BLS lists 8,370 jobs and a median of $164,740 per year, or $44,510 above the U.S. Data Scientists median. The U.S. job estimate is 262,440.
5. Durham-Chapel Hill, NC has a Data Scientists location quotient of 2.28. BLS lists 1,320 jobs and a median of $106,500 per year, or $13,730 below the U.S. Data Scientists median. The U.S. job estimate is 262,440.
6. Salt Lake City-Murray, UT has a Data Scientists location quotient of 2.13. BLS lists 2,970 jobs and a median of $114,990 per year, or $5,240 below the U.S. Data Scientists median. The U.S. job estimate is 262,440.
Precise employer-title matches for Data Scientists
Matched from employer-posted title terms, not a BLS classification. Confirm the listing's location because this occupation-wide set is not a U.S. place filter. A posted range never changes the official distribution above.
