Data Science Job Salary Guide: What the Data Actually Shows in 2026

The most reliable answer starts with the U.S. Bureau of Labor Statistics: data scientists earned a median annual wage of $112,590 as of May 2024, with the middle of the field earning well above or below that depending on industry, experience, and location. Anything more specific than that median figure comes from private, less rigorous sources, which is worth being explicit about rather than presenting every number found online as equally authoritative.

The Short Answer: How Much Do Data Science Jobs Pay?

1. The median annual wage for data scientists was $112,590 as of May 2024, per the U.S. Bureau of Labor Statistics, the most authoritative available figure for this occupation.
2. Pay varies widely around that median: the lowest 10% of earners made less than $63,650, while the highest 10% made more than $194,410, reflecting real differences in experience, industry, and location.
3. Industry matters meaningfully. Computer systems design and management of companies pay above the median, while other industries pay somewhat below it, detailed further down.
4. Experience level, specific role type, and location all shift pay considerably beyond what the single median figure captures, covered in the sections below using clearly-attributed secondary sources.

What the Official BLS Data Actually Shows

According to the BLS Occupational Outlook Handbook, data scientists earned a median annual wage of $112,590 ($54.13 per hour) as of May 2024. The wage distribution around that median is wide: the lowest-earning 10% of data scientists made less than $63,650 per year, while the highest-earning 10% made more than $194,410. That spread reflects genuinely different realities within a single job title, from an early-career analyst-adjacent role at a smaller company to a senior specialist at a large, well-resourced employer.

Industry also shapes pay meaningfully within this same occupation. Per BLS's May 2024 industry breakdown, data scientists working in computer systems design earned a median of $128,020, those in management of companies and enterprises earned $126,940, scientific research and development earned $120,090, management consulting services earned $110,240, and insurance carriers earned $108,920. The pattern is fairly intuitive: industries where data science work sits closest to the core product or research function tend to pay above the field-wide median, while industries where it functions more as a supporting analytical function tend to sit closer to or below it.

Salary by Experience Level

The BLS occupational summary doesn't break its figures out by years of experience, so the ranges below come from a 2026 staffing-industry salary guide (Motion Recruitment) rather than a government survey, and should be read as directional, recruiter-informed figures rather than the same caliber of data as the BLS median above:

The same staffing guide's data engineer breakdown, from a separate 2026 guide by the same recruiting firm category, gives a clearer entry-to-senior progression specifically: roughly $80,000-$105,000 for 0-3 years of experience, $119,000-$150,000 for 4-6 years, and $147,000-$179,000+ for 7 or more years, with staff/principal-level roles reaching $175,000-$220,000 or more in base salary alone. These figures notably exclude equity and bonus, which the same source notes can push total compensation at major technology companies considerably higher, into the $250,000-$350,000 range for senior roles at the largest, most well-resourced employers specifically.

Why Salary Sources Give Such Different Numbers

Anyone who's searched for data science salary figures has likely noticed that different sources disagree, sometimes by tens of thousands of dollars for what looks like the same role. That's not random noise; it reflects genuinely different data collection methods. BLS OEWS data (used for the median and percentile figures above) comes from a large-scale, systematic employer survey covering the full breadth of the occupation nationally, which makes it the most methodologically rigorous available source, though it updates on an annual cycle and reports base wage only, not total compensation. Staffing-industry guides, like the ones cited above for experience-level breakdowns, are built from a specific recruiting firm's own placement and client data, which tends to skew toward the roles and companies that firm actually places candidates into, useful for directional, more current figures but not a random national sample. Self-reported salary aggregators (a category this article deliberately doesn't cite, given the difficulty of verifying self-reported figures) add another layer of variability, since respondents self-select and self-report both role and pay level without independent verification. None of these sources is "wrong" exactly; they're measuring somewhat different things, which is why the honest approach is citing each for what it's actually good at, government data for the reliable national baseline, staffing-industry data for more current, granular breakdowns, rather than blending them into one falsely precise number.

Salary by Role Type

Pay also varies by which specific role within the broader data science umbrella you hold, covered in full in What Is a Data Science Job? Roles, Responsibilities & Job Titles Explained. Based on the sourced ranges above and general market patterns, data analyst roles tend to sit below generalist data scientist roles, reflecting a typically lighter statistics and machine learning requirement. Data engineer and ML engineer roles tend to command pay comparable to or above generalist data scientist roles at similar experience levels, reflecting the additional software engineering skill those roles require. Research scientist roles, concentrated at larger companies and often requiring a graduate degree, tend to sit at or above the higher end of the generalist data scientist range, though specific, reliable figures for this narrower role are harder to source independently and aren't included here to avoid presenting an unverified number as fact.

Does Location Still Matter for Salary?

Yes, meaningfully, though remote work has narrowed some of the historical gap. The same staffing guide cited above shows considerable city-level variation for data scientist roles: notably higher ranges in San Francisco and Seattle compared to lower-cost markets, with remote-specific roles often landing in a middle range between the highest and lowest-cost physical markets. This is consistent with the broader shift toward remote and hybrid data science work covered in Are Data Science Jobs Remote? How to Land a Remote Data Science Role: location still matters, but a specific company's remote pay policy (whether it pays a flat national rate or adjusts by the employee's location) now matters as much as the general geographic market.

Does a Degree Change Your Salary?

Somewhat, though less than many candidates assume. A graduate degree is more consistently associated with higher pay specifically for research scientist and some specialized quantitative roles, where a master's or PhD is close to a baseline expectation. For generalist data scientist, data analyst, and engineering-track roles, actual experience, a strong portfolio, and specific technical skills tend to matter at least as much as degree level once you're past the initial hiring screen. Can You Get a Data Science Job Without a Degree, Certificate, or Experience? covers this tradeoff directly for anyone weighing whether to pursue a degree specifically for salary reasons.

How to Actually Increase Your Data Science Salary

1. Specialize toward a higher-paying adjacent role if your interests allow it. Data engineering and ML engineering roles, per the sourced figures above, tend to command pay at or above generalist data scientist roles at comparable experience levels.
2. Target industries that consistently pay above the field median, per the BLS industry breakdown above, particularly computer systems design and management of companies and enterprises, rather than assuming all industries pay similarly for the same title.
3. Negotiate based on total compensation, not just base salary, especially at larger technology companies where equity and bonus can represent a substantial share of total pay, per the staffing-guide figures above.
4. Build a portfolio that demonstrates the specific, higher-value skills tied to your target role, covered in the Career Entry pillar guide, since demonstrated capability moves pay more reliably at the negotiation stage than credentials alone.

Mistakes People Make Reading Salary Data

1. Treating a single quoted number as the whole picture, when the BLS data alone shows a roughly $130,000 spread between the 10th and 90th percentile for the same job title.
2. Not distinguishing government survey data from private, self-reported, or staffing-firm data, which vary considerably in methodology and can produce meaningfully different numbers for the same role.
3. Comparing base salary figures from one source against total compensation figures from another, especially when researching large technology companies where equity and bonus can substantially change the real number.
4. Assuming national figures apply evenly everywhere, when industry, city, and a specific company's remote-pay policy can each shift the realistic range considerably from a single national median.

FAQ

How much do data science jobs pay on average?

The median annual wage for data scientists was $112,590 as of May 2024, per the U.S. Bureau of Labor Statistics, with a wide range around that median: under $63,650 for the lowest-earning 10% and over $194,410 for the highest-earning 10%.

What is the highest-paying data science role?

Among the roles with reasonably sourced data, senior data engineer, staff/principal-level engineering roles, and senior data scientist roles at large technology companies tend to reach the highest total compensation, particularly once equity and bonus are included, though role-specific figures vary by source and methodology.

Does remote work pay less than in-office data science jobs?

Not necessarily. It depends heavily on a specific company's remote-pay policy: some pay a flat national rate regardless of location, while others adjust based on where the employee lives, which can land above or below a comparable in-office role in a given city.

Do you need a master's degree to earn a higher data science salary?

Only meaningfully for certain specialized roles, particularly research scientist and some quantitative positions. For most generalist data scientist, analyst, and engineering-track roles, experience and demonstrated skill tend to matter at least as much as degree level for pay once you're past the initial hiring screen.

How much do entry-level data science jobs pay?

Entry-level pay typically sits below the mid-level ranges cited in staffing-industry guides and closer to the lower portion of the BLS's overall wage distribution, though exact entry-level figures vary considerably by role, industry, and location.

Why do different salary websites show such different numbers for the same job?

Because they measure different things. Government survey data (BLS) covers the full national occupation systematically but reports base wage only. Staffing-industry guides reflect a specific recruiting firm's own placement data, which skews toward the roles and companies that firm actually works with. Self-reported aggregators add further variability since neither the role nor the pay figure is independently verified. Comparing figures across source types without accounting for this is the most common reason two numbers for the "same" role look inconsistent.

Your Next Step

Browse current data science, analyst, and engineering openings across companies and industries on finddatasciencejobs.com.

Read next:

• What Is a Data Science Job? Roles, Responsibilities & Job Titles Explained
• Are Data Science Jobs Remote? How to Land a Remote Data Science Role