Dataford · Salary Report
The Data & AI Salary Report 2026
We analyzed 15,000+ compensation bands across data, analytics, and AI roles to see what these jobs actually pay in 2026 — by role, by level, and where the AI premium really sits. Here is what the numbers show.
Summary · Key findings
Machine Learning Engineer is the highest-paid data role. At a $225K median, it sits ahead of every classic data title — about $56K above Data Scientist and $105K above Data Analyst. The roles that touch the model layer pay the most.
The AI premium is real, and it is a full tier. ML Engineer, AI Engineer, and the AI-native titles cluster around $170K–$225K, while classic analytics roles — Data Analyst at $120K, Business Analyst at $108K — sit a clear tier below.
Level moves pay more than title does. Median total comp climbs from $170K at L3 to $385K at L7. Two and a half levels of seniority is worth more than any jump between data titles at the same level.
The spread inside a single role is enormous. A Data Scientist at the 90th percentile earns $494K — about 4.6× the $107K a Data Scientist at the 10th percentile earns. The company and the level you land decide far more than the title on the offer.
Analytics has the widest floor-to-ceiling range. Data Analyst pay starts modestly ($71K at the 10th percentile) but reaches $446K at the 90th — the analysts who climb into senior, product, and AI-adjacent seats close most of the gap to engineering.
Pay is the question every candidate eventually asks and the one with the worst public answers. Numbers float around, but most are a single data point dressed up as a benchmark.
Dataford tracks compensation across more than 1,000 companies and 50-plus roles. For this report we took every salary band we hold in US dollars, trimmed the obvious outliers, and computed real medians and percentiles — 15,293 bands in total. Total compensation here means base, bonus, and equity combined, the way an offer actually arrives.
We report medians rather than averages, because a handful of very large packages drag an average upward and make every role look better paid than it is. Where the spread matters, we show the 10th to 90th percentile band so you can see the floor and the ceiling, not just the middle. The full method is at the end.
Roles
What each role pays
The ranking is consistent and it has a logic to it: the closer a role sits to building models, the more it pays. ML Engineer leads at a $225K median, and the gap to Data Analyst at $120K is more than a hundred thousand dollars.
The dot marks the median; the bar spans the 10th to 90th percentile. Total comp combines base, bonus, and equity.
Notice the bars, not just the dots. Two roles with similar medians can have very different ceilings. Data Scientist and Data Analyst both stretch past $440K at the top, which is where senior and staff-equivalent seats live. The title sets the middle of the range; the company and level set how far the top end runs.
Levels
Level moves pay more than title
If you optimize one thing for compensation, optimize for level. The median climbs from $170K at L3 to $385K at L7, and that ladder outpaces almost any sideways move between data titles.
The jumps are not evenly spaced. The step from L4 to L5 — roughly $204K to $271K — is the largest single rung, and it is the one where scope shifts from doing the work to owning the outcome. Candidates who understand that an interview loop is really a level-calibration exercise tend to negotiate from a stronger place.
Finding
The AI premium, measured
Take the Data Analyst median of $120K as a baseline and the premium for moving toward the model layer is easy to see. ML Engineer earns $105K more; AI Engineer and Data Scientist each clear $49K more.
Measured as the difference between each role's median total comp and the Data Analyst median.
This is the clearest argument for the bridge that data professionals keep hearing about. The skills do not have to change completely. An analyst who learns to build with models, or a data scientist who moves into ML engineering, is moving up a pay tier, not just changing a job title.
Outlook
What this means for your next offer
The headline number for your title is the least useful figure in this report. The spread is the story, and the spread is mostly explained by two things you can influence: the level you interview at and the company tier you target.
If you are an analyst or data scientist, the fastest raise is usually not a new title at the same level — it is leveling up, or moving toward the AI-adjacent work that pays a tier more for an overlapping skill set. If you are already an engineer, the question is whether you are being calibrated at the right level, because two rungs of seniority is worth more than any lateral move on this chart.
Either way, the offer you get is set in the interview loop, where your level is decided. That is the part you can prepare for.
Prepare for the loop that sets your level
Practice the real questions companies ask for your role, with worked solutions and company-specific interview guides.
Explore interview guidesFAQ
Frequently asked questions
What is the highest-paying data role in 2026?+
Machine Learning Engineer, with a median total compensation of about $225K — ahead of Data Scientist ($169K), Data Engineer ($152K), and Data Analyst ($120K). The roles closest to building and shipping models command the most.
How much does a Data Scientist make?+
The median Data Scientist total compensation is around $169K. The range is wide: roughly $107K at the 10th percentile and $494K at the 90th, depending heavily on company, level, and location.
Is there really an AI pay premium?+
Yes. ML Engineer, AI Engineer, and the AI-native titles cluster around $170K–$225K, a full tier above classic analytics roles like Data Analyst ($120K) and Business Analyst ($108K). The premium tracks how close the role sits to the model layer.
How much does compensation grow with level?+
A lot. Median total comp rises from about $170K at L3 to $385K at L7 (staff/principal). Moving up two to three levels is worth more than switching to a higher-paid data title at the same level.
Why is the pay range for one role so wide?+
Total compensation bundles base, bonus, and equity, and it varies sharply by company tier, level, and location. For a single role like Data Scientist, the 90th percentile can be more than four times the 10th — so the offer you get depends far more on where and at what level you land than on the title itself.
Methodology
How this report was built
This report is built on Dataford's compensation dataset: 15,293 total-compensation bands in US dollars, each combining base, bonus, and equity for a company, role, and level. We trimmed bands below $40K and above $2M to remove data-entry errors and a handful of extreme packages that would distort the percentiles.
All headline figures are medians, computed per role and per level, with the 10th and 90th percentiles shown where the spread matters. We use medians rather than averages so that a few very large packages do not pull the numbers upward. Roles are shown only where we hold at least 30 bands; the level ladder uses standardized level codes from L3 to L7.
Compensation reflects the companies and roles in our dataset and may not match any single market or location. Figures are current as of June 2026.