Data Scientists: Arizona vs Washington pay

Percentile	Published wages	Arizona	Washington
10th	Arizona$65,200Washington$79,930	$65,200	$79,930
25th	Arizona$80,820Washington$105,240	$80,820	$105,240
Median	Arizona

Place	Employment	Jobs per 1,000	Location quotient	Employment RSE
Arizona	4,560Jobs per 1,000: 1.41 Location quotient: 0.83	1.41	0.83	21.1%
Washington	9,600Jobs per 1,000: 2.71 Location quotient: 1.6	2.71	1.6	3.4%

75th	Arizona$134,070Washington$208,320	$134,070	$208,320

90th	Arizona$161,730Washington$220,040	$161,730	$220,040

Start with the answer

Data Scientists pay in Arizona and Washington

The May 2025 BLS OEWS median for Data Scientists is $107,240 per year in Arizona. It is $163,350 per year in Washington. Arizona is 34.3% below Washington on the published median.

Both rows use SOC 15-2051 and the same annual wage metric. Median means the middle of each area's published wage spread. It is not one worker's pay. The larger median is not proof that one place gives a better job or a better result. It is one wage fact from one source period.

BLS lists 4,560 wage and salary jobs in Arizona and 9,600 in Washington. Those are survey job counts, not open posts. Read pay, job scale, local share, and price level as separate facts before you make a choice.

Evidence: bls-oews-may-2025 · Rule: same-soc-same-metric-pair · nominal-median-gap · employment-not-openings

Pay range

Compare the full Data Scientists wage ladder

A median can hide a wide range. The 10th, 25th, 50th, 75th, and 90th percentile points show more of each area's spread. A percentile is a rank point. It is not a career stage. The 10th point does not mean new-worker pay. The 90th point does not mean a wage that comes after a set number of years.

The lower half can differ from the upper half. One area can lead at the median and trail at another point. Read each field against the same field in the other area. Do not compare one area's 75th percentile with the other area's median. That would answer a different question.

A posted job range can sit below, across, or above these marks. That does not make the post wrong. A listing can differ by employer, level, hours, field, or work rules. Keep the listing tied to that employer. Keep the BLS range tied to the whole role-place row.

10th percentile: Arizona is $65,200 per year; Washington is $79,930 per year. Arizona is 18.4% below Washington at this point.
25th percentile: Arizona is $80,820 per year; Washington is $105,240 per year. Arizona is 23.2% below Washington at this point.
Median: Arizona is $107,240 per year; Washington is $163,350 per year. Arizona is 34.3% below Washington at this point.
75th percentile: Arizona is $134,070 per year; Washington is $208,320 per year. Arizona is 35.6% below Washington at this point.
90th percentile: Arizona is $161,730 per year; Washington is $220,040 per year. Arizona is 26.5% below Washington at this point.

Evidence: bls-oews-may-2025 · Rule: paired-percentile-fields · same-field-comparison · posted-pay-separation

Shape of pay

Mean and median tell two parts of the Data Scientists story

Arizona has a mean of $111,950 per year and a median of $107,240. Washington has a mean of $158,040 per year and a median of $163,350. Mean adds all wage values and splits the total by the number of jobs behind the estimate.

Arizona's mean is 4.4% above its median on the same published median. Washington's mean is 3.3% below its median on the same published median. The gaps show that each wage spread has a shape. They do not explain why the shape differs.

Use median for the middle. Use mean to see how the full set pulls. Use percentiles to see more of the spread. None of the three is a pay promise. A person, team, or listing can sit far from all three for reasons that are outside these BLS rows.

Evidence: bls-oews-may-2025 · Rule: paired-mean-median · mean-median-gap · no-causal-inference

Job scale

How the two Data Scientists job bases differ

Arizona has 4,560 estimated Data Scientists jobs. Washington has 9,600. The larger count shows a larger covered job base in this source. It does not show more open roles, faster hiring, or a better chance for one applicant.

Arizona has 1.41 Data Scientists jobs per 1,000 local jobs. Washington has 2.71 per 1,000. This rate helps compare places of different size. It is not a job-post rate and does not use the MeritLog catalog.

The employment RSE is 21.1% in Arizona and 3.4% in Washington. RSE means relative standard error. It is a survey precision flag. It is not the chance that the job count or one person's pay is right.

Evidence: bls-oews-may-2025 · Rule: paired-employment · paired-jobs-per-thousand · employment-rse-field-scope

Local share

Where Data Scientists takes a larger share of jobs

The location quotient is 0.83 in Arizona and 1.6 in Washington. 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 it is smaller.

Arizona's quotient is 48.1% below Washington's. That gap is about the job mix. It is not a wage gap, a job-count gap, or a growth claim.

Read location quotient with employment. A small place can have a high share and few jobs. A large place can have many jobs and a share near the national mark. Then read pay as its own field. The three measures do not form one score.

Evidence: bls-oews-may-2025 · Rule: paired-location-quotient · quotient-gap · metric-separation

BEA price view

How price levels differ between Arizona and Washington

BEA's 2024 all-items price index is 100.7 in Arizona and 107.0 in Washington. The U.S. level is 100. A higher number means the broad local price level is higher than the national mark. It does not say a place is good or bad.

Housing is 106.8 in Arizona and 126.0 in Washington. Goods are 95.4 and 104.4. Utilities are 92.3 and 92.9. Other services are 102.8 and 103.9. The parts can move in different ways.

The wage period is May 2025. The price period is 2024. WageScope keeps those clocks visible. It uses the all-items index for one clear proxy. It does not mix BEA prices into the BLS source row, and it does not claim the two releases describe the exact same month.

Evidence: bea-rpp-2024 · bls-oews-may-2025 · Rule: paired-rpp-fields · rpp-us-baseline · source-period-separation

Price-level proxy

Compare the medians in national-price dollars

WageScope divides Arizona's $107,240 median by 100.7 divided by 100. The result is $106,519 per year. It divides Washington's $163,350 median by 107.0 divided by 100. That result is $152,645 per year.

Arizona's proxy is 30.2% below Washington's. The nominal gap is 34.3%. The order can stay the same or change after the price-level step. Both results must keep their labels.

The proxy is a narrow math tool. It is not take-home pay. It does not add taxes, benefits, household size, debt, commute, schools, safety, climate, or taste. It does not set a wage target. Use it to see how one broad price index changes the wage view, then check your own costs outside WageScope.

Evidence: bls-oews-may-2025 · bea-rpp-2024 · meritlog-bounded-comparison-v1 · Rule: price-level-adjusted-formula · nominal-adjusted-gap-separation · proxy-limits

Same-proxy pay

What equal price-level pay would look like in the other place

$107,240 per year in Arizona has the same national-price-dollar proxy as $113,989 per year in Washington. The math keeps Arizona's proxy fixed, then applies Washington's all-items price index.

$163,350 per year in Washington has the same proxy as $153,678 per year in Arizona. This reverse view is useful because the answer depends on which wage you start with. It is not a claim about an offer in either place.

Equivalent nominal pay uses one broad price measure. It does not know what a person buys. It does not know rent, family size, debt, taxes, travel, benefits, or savings. Treat the result as a clean source-backed comparison, not a full move plan or a number an employer must match.

Evidence: bls-oews-may-2025 · bea-rpp-2024 · meritlog-bounded-comparison-v1 · Rule: equivalent-nominal-value-forward · equivalent-nominal-value-reverse · proxy-limits

Data care

Check the survey marks before you use the gap

The mean-wage RSE is 3% in Arizona and 2% in Washington. The employment RSE is 21.1% and 3.4%. RSE is a precision flag for the field it sits beside. It is not a score for the whole page.

BLS does not publish the same RSE field for the median and each percentile. WageScope does not move a mean RSE to the median. It does not use an employment RSE as a wage care mark. Each value stays tied to its source field. Missing or top-coded values also keep their source state.

A later BLS or BEA release can change the result. MeritLog checks the official source clocks before promotion. The page cannot call its data current when a newer complete release is waiting for review. Current job cards have their own seen date and do not make the government data newer.

Evidence: bls-oews-may-2025 · Rule: paired-rse-field-scope · no-rse-transfer · independent-source-freshness

Use the result

A short check list for Arizona and Washington

Start with the same Data Scientists wage field. Then read the full percentile spread. Check job count, jobs per 1,000, and location quotient. Add the 2024 all-items and housing price levels. Read the national-price-dollar proxy with its limits. Keep the BLS and BEA dates in view.

Next, inspect one current employer-title match in each place when an exact public place match exists. A job post can show work level, hours, location rules, and pay that the BLS row cannot. The post is dated employer evidence. It does not change the survey median or prove a broad trend.

Last, use your own facts. Compare the job needs with work you have done and can support. A wage gap cannot show fit. A price-level gap cannot show fit. A job count cannot show fit. MeritLog's résumé path starts only after you choose a listing and confirm the work facts you want to use.

Evidence: bls-oews-may-2025 · bea-rpp-2024 · meritlog-public-job-boundary-v1 · Rule: comparison-checklist · job-source-separation · confirmed-resume-evidence

Limits

What this Data Scientists comparison does not decide

The page does not choose Arizona or Washington for you. It does not say what one worker earns. It does not promise a job, offer, raise, or move result. It does not say why the medians differ. It does not turn the larger wage, job count, quotient, or proxy into a place score.

The comparison leaves out people who work for themselves and other work outside the OEWS scope. It does not add taxes, take-home pay, benefits, stock, bonus pay, hours, travel, remote work, or a household budget. Current listings are separate and can be empty even when the government rows are complete.

Use this page for a bounded choice. Compare the same May 2025 BLS role and metric across two exact areas. Add the separate 2024 BEA price view. Inspect dated employer-title matches. Then build a résumé from facts you have confirmed. Keep each source tied to the question it can answer.

Evidence: bls-oews-may-2025 · bea-rpp-2024 · meritlog-public-job-boundary-v1 · Rule: no-place-choice · no-personal-pay-claim · source-purpose-separation

Data Scientists: Arizona vs Washington

Same occupation, same annual wage metric

The winner can change when local prices differ

Housing and all-items indexes explain the adjustment

Equivalent nominal pay, using price levels only

Employment scale and local concentration

Compare another occupation or pair

Data Scientists: Arizona and Washington guide

Data Scientists in Arizona and Washington: common questions

Compare Data Scientists listings by place

Arizona

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Washington

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Current jobs

Tailor for a real Data Scientists job, not a generic wage number.

Data Scientists pay in Arizona and Washington

Compare the full Data Scientists wage ladder

Mean and median tell two parts of the Data Scientists story

How the two Data Scientists job bases differ

Where Data Scientists takes a larger share of jobs

How price levels differ between Arizona and Washington

Compare the medians in national-price dollars

What equal price-level pay would look like in the other place

Check the survey marks before you use the gap

A short check list for Arizona and Washington

What this Data Scientists comparison does not decide

Compare Data Scientists listings by place

Arizona

We could not load these listings.

Washington

Exact location filter unavailable.

Privacy choices

Data Scientists: Arizona vs Washington

Same occupation, same annual wage metric

The winner can change when local prices differ

Housing and all-items indexes explain the adjustment

Equivalent nominal pay, using price levels only

Employment scale and local concentration

Compare another occupation or pair

Data Scientists: Arizona and Washington guide

Data Scientists in Arizona and Washington: common questions

Compare Data Scientists listings by place

Arizona

We could not load these listings.

Washington

Exact location filter unavailable.

Current jobs

Tailor for a real Data Scientists job, not a generic wage number.

Data Scientists pay in Arizona and Washington

Compare the full Data Scientists wage ladder

Mean and median tell two parts of the Data Scientists story

How the two Data Scientists job bases differ

Where Data Scientists takes a larger share of jobs

How price levels differ between Arizona and Washington

Compare the medians in national-price dollars

What equal price-level pay would look like in the other place

Check the survey marks before you use the gap

A short check list for Arizona and Washington

What this Data Scientists comparison does not decide

Compare Data Scientists listings by place

Arizona

We could not load these listings.

Washington

Exact location filter unavailable.