---
title: "Data Scientist Salary Guide 2026 | Metaintro"
canonical: "https://www.metaintro.com/blog/data-scientist-salary-2026-pay-by-experience-and-city"
language: "en"
author: "drashtigarach"
published: "2026-06-29T15:47:30.000Z"
modified: "2026-09-29T16:22:13.164Z"
---

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# Data Scientist Salary Guide 2026, Pay by Experience and City

Data scientists earned a median of $112,590 in 2024 per BLS, with FAANG pay above $335K. See 2026 data scientist pay by experience, city, and industry.

[![Drashti Garach](https://cdn.metaintro.com/rs:fill:40:40/q:72/plain/images/5719d740-e510-42bc-8017-e040d145f35f_1766029465094.png)Drashti Garach @DrashtiGarach](/blog/author/drashtigarach)

[June 29, 2026](/blog/archive/2026/06)Updated September 29, 202615 min read

![Data Scientist Salary Guide 2026, Pay by Experience and City](https://cdn.metaintro.com/rs:fill:1200:675/q:78/plain/images/5111.png)

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If you want one number to anchor your expectations, start here. Data scientists earned a national median wage of $112,590 in May 2024, according to the U.S. [Bureau of Labor Statistics](https://www.bls.gov/ooh/math/data-scientists.htm), and the field is still one of the fastest growing in the country. But that single figure hides an enormous spread, because the same job title can pay under $100,000 at one employer and well past $400,000 at another. The difference comes down to three levers you can actually influence: your experience level, the city you work in, and the industry that signs your paycheck. At [Metaintro](https://www.metaintro.com), we track this kind of compensation data so job seekers can walk into a negotiation knowing exactly where they stand. This guide breaks down 2026 data scientist pay across all three levers, then shows you how to push your own number higher, with every figure tied to its primary source so you can verify it yourself.

## What Is the Average Data Scientist Salary in 2026?

The cleanest baseline comes from the [Bureau of Labor Statistics](https://www.bls.gov/ooh/math/data-scientists.htm), whose Occupational Employment and Wage Statistics program pegged the median data scientist wage at $112,590 in May 2024, the most recent full-year reading. Median means half the field earned more and half earned less, so it is a far more honest anchor than the eye-popping averages that get passed around online. The same [BLS data](https://www.bls.gov/oes/current/oes152051.htm) shows the lowest ten percent of data scientists earned less than $63,650, while the highest ten percent earned more than $194,410. That bottom-to-top range, roughly $130,000 of spread within a single occupation code, is the whole story of this guide.

One trap worth avoiding is treating the "average" data scientist salary as your expected pay. Averages get pulled upward by a small number of very large packages at elite firms, so headlines quoting a $160,000 or $200,000 average can leave a new hire badly disappointed when the real offers land closer to the median. The percentile data is the more useful map, because it tells you where you realistically sit given your background. Cross-referencing the BLS figures against [O*NET's data scientist profile](https://www.onetonline.org/link/summary/15-2051.00), which draws on the same government wage series, gives you a second confirmation of the national picture before you ever talk numbers with a recruiter.

Staffing firm [Robert Half](https://www.roberthalf.com/us/en/job-details/data-scientist) puts the 2026 starting range a notch higher for the candidates it places, from about $121,750 to $182,500, with a midpoint near $153,750. The gap between the BLS median and the Robert Half midpoint is not a contradiction. Robert Half samples placements at companies actively recruiting for specialized talent, while the BLS counts every data scientist in the economy, including those at smaller firms and in lower-cost regions. For your own planning, treat the BLS median as the floor of a healthy market rate and the Robert Half range as what a competitive employer should offer a fully qualified hire. If you are still early in the field, our guide on [career metrics and market value](https://www.metaintro.com/blog/career-metrics-earning-potential-market-value-2026) explains how to translate these benchmarks into a number you can defend.

## How Much Do Data Scientists Earn by Experience Level?

Experience is the single biggest driver of data scientist pay, and the curve is steep. At the entry level, a new data scientist with a strong portfolio but limited industry time tends to land near the bottom of the [Robert Half](https://www.roberthalf.com/us/en/insights/salary-guide/technology) starting range, around $121,750, or closer to the BLS tenth-percentile figure of $63,650 at smaller and non-tech employers. The honest takeaway for newcomers is that your first title matters less than your first employer, because a mainstream company and a big-tech company can pay an entry-level data scientist double-digit thousands apart for the same work. If you are breaking in from an adjacent role, our walkthrough on how to [become a data analyst without a degree](https://www.metaintro.com/blog/become-data-analyst-without-degree) and the [essential data analyst skills](https://www.metaintro.com/blog/essential-data-analyst-skills-2025) to master first both feed naturally into a data science track.

Mid-career data scientists, usually those with three to six years of shipping models into production, cluster around the Robert Half midpoint of $153,750 and the BLS median of $112,590, depending on industry and city. This is where the curve starts to bend upward fast. Senior data scientists, the ones who own a problem area end to end and mentor juniors, routinely sit in the high $100,000s to low $200,000s at mainstream firms, matching the BLS top-decile mark of $194,410. The real jump happens at staff and principal levels. At [Google](https://www.levels.fyi/companies/google/salaries/data-scientist), tracked by [Levels.fyi](https://www.levels.fyi/t/data-scientist), a senior L5 data scientist carries a median total package near $371,045, and an L6 staff data scientist near $445,109, with the most senior L8 packages reaching $895,000. The lesson is structural: after the senior rung, pay growth comes less from raises and more from leveling up your title and your scope.

For your own trajectory, this means the highest-leverage years are not your first two but the move from mid to senior and then from senior to staff. Each rung roughly resets your market value rather than adding a percentage to it, which is why two data scientists who started together can be tens of thousands apart five years in. Knowing how to negotiate that jump, which our guide on [salary negotiation tactics that work](https://www.metaintro.com/blog/salary-negotiation-tactics-that-work-2026) covers in detail, is worth more than any annual cost-of-living bump, and the difference compounds for the rest of your career.

## Which Cities and Regions Pay Data Scientists the Most?

Where you sit changes your paycheck almost as much as how long you have worked. The [Bureau of Labor Statistics](https://www.bls.gov/oes/current/oes152051.htm) consistently ranks California, Washington, and New York among the top-paying states for data scientists, and the metro picture is even sharper. The San Francisco Bay Area and San Jose sit at the top because that is where the highest-paying employers concentrate, followed by the Seattle region, home to [Amazon](https://www.metaintro.com/blog/amazon-careers-guide-2026-roles-pay-hiring-tips) and other tech giants, and the New York City area, where finance and tech compete for the same talent. [Levels.fyi](https://www.levels.fyi/companies/google/salaries/data-scientist/locations/new-york-city-area) shows the same Google data scientist role ranging from roughly $142,000 to over $431,000 in the New York City area alone, which tells you how wide a single city can swing.

The catch every job seeker should internalize is cost of living. A $200,000 offer in San Jose and a $140,000 offer in a lower-cost metro can leave you with similar real spending power once rent and taxes come out, so the headline number is only half the math. Run any geographic comparison through take-home pay, not gross, and our [gross pay versus net pay guide](https://www.metaintro.com/blog/gross-pay-vs-net-pay-guide) shows exactly how those deductions stack up. Remote roles complicate this further, because many employers now set pay bands by your home location rather than the company headquarters, which can erase a coastal premium entirely. Before you accept a relocation or a remote band, treat the city as a salary variable in its own right, and never let an employer hide the range. If a posting omits a band, our piece on why [workers skip job postings with no salary range](https://www.metaintro.com/blog/workers-skip-job-postings-no-salary-range-2026) explains why walking away is often the rational move.

## Does Industry Change What a Data Scientist Makes?

Two data scientists with identical skills can earn very different money depending on who employs them, and industry is the reason. The [Bureau of Labor Statistics](https://www.bls.gov/ooh/math/data-scientists.htm) shows that data scientists in scientific research and development services earned a higher median of $120,090, above the overall $112,590 figure, while sectors like government, education, and traditional retail tend to pay below the median. The pattern is intuitive once you see it: industries where data directly drives revenue, such as software, advertising technology, finance, and quantitative trading, compete hardest for talent and pay the most. Industries where data is a support function rather than the product pay closer to the national floor.

Within the higher-paying tiers there is still a clear pecking order. Quantitative finance and high-frequency trading firms sit at the very top because a better model translates directly into trading profit, software and advertising technology follow closely, and healthcare and biotech analytics pay competitively while often offering more stable hours. For job seekers, this means industry choice is a salary decision, not just a culture decision. Moving from a public-sector analytics team to a high-margin software company can be worth more than years of internal promotions, because you are repricing your skills into a market that values them more.

The [2026 tech salary guide](https://www.metaintro.com/blog/2025-tech-salary-guide-it-hiring-compensation) and our broader [IT careers salary guide](https://www.metaintro.com/blog/it-careers-2025-salary-guide) lay out how those sector premiums compare across technical roles. The flip side is stability and pace, because the highest-paying industries often demand the most intense workloads. If you are weighing whether the premium is worth it, our look at [which college degrees pay off](https://www.metaintro.com/blog/which-college-degrees-pay-off-2026) frames the same trade-off from the education-investment angle, and the logic carries straight into mid-career industry switches.

## How Do FAANG and Big Tech Pay Compare to the Median?

Big tech is where the data scientist pay ceiling lives, and the gap to the national median is dramatic. According to [Levels.fyi](https://www.levels.fyi/companies/google/salaries/data-scientist), data scientist compensation at [Google](https://www.levels.fyi/companies/google/salaries/data-scientist) ranges from about $190,000 at the entry L3 level to $895,000 at the most senior L8 level, with a median package near $335,000. At [Meta](https://www.levels.fyi/companies/meta/salaries/data-scientist), the range runs from roughly $164,000 at IC3 to more than $1.11 million at IC8, with a median near $340,000. Even the median package at these companies sits at roughly three times the national BLS median, which is why so many ambitious data scientists treat a big-tech offer as the goal.

The structure matters as much as the size. Big-tech packages are built from three parts: a base salary, an annual cash bonus, and equity that vests over several years. At senior levels, equity often becomes the largest single component, which means your real pay rises and falls with the stock and is rarely guaranteed in full. A $400,000 offer that is sixty percent stock is a very different proposition from one that is mostly base, and our breakdown of [startup engineer salaries and the equity gap](https://www.metaintro.com/blog/startup-software-engineer-salaries-200k-base-pay-25-percent-increase-equity-gap-2026) shows how that distinction plays out when you move down to earlier-stage companies. The adjacent machine learning track pays in the same tier, and our profiles of a [machine learning engineer role](https://www.metaintro.com/blog/software-engineer-machine-learning-whatnot) and a [PlayStation machine learning engineer](https://www.metaintro.com/blog/playstation-machine-learning-engineer) hiring path show how closely the two career ladders mirror each other. If a big-tech package is your target, read the offer by component, not by the headline total, and ask how the equity is valued and over how many years it vests before you compare two numbers.

## What Skills and Moves Raise a Data Scientist's Pay?

The fastest way to move your number is to make your skills more scarce, and in 2026 that means leaning into applied AI and machine learning. Research from [Robert Half](https://www.roberthalf.com/us/en/insights/salary-guide/technology) shows roles in artificial intelligence, machine learning, and data science are projected to see higher-than-average starting salary gains of about 4.1 percent into 2026, ahead of much of the broader tech market. Practitioners who can deploy models, work with large language models, and own the full pipeline from data to production command a clear premium over those who stop at dashboards and reporting. Our deep dive on the [AI skills salary premium](https://www.metaintro.com/blog/ai-skills-salary-premium-prompt-engineering-mlops) maps exactly which capabilities carry the biggest pay bump, and the research on [workers using AI earning more](https://www.metaintro.com/blog/workers-using-ai-earning-more-2026-research) backs it with hard numbers.

Skills alone are not enough, because most underpaid data scientists are leaving money on the table at the offer stage rather than the skill stage. Three moves do the most work. First, negotiate every offer, since the gap between the first number and the final number is often five figures, and our [salary negotiation tactics](https://www.metaintro.com/blog/salary-negotiation-tactics-that-work-2026) and step-by-step guide on [how to negotiate your salary for a higher offer](/blog/how-to-negotiate-salary) walk through the scripts. Second, chase leveling, not just raises, because moving from senior to staff at a big-tech firm can add six figures in a single jump, far more than any merit cycle. Third, build a visible portfolio that proves you ship business impact, which is the evidence recruiters actually pay for.

There is also a quieter move that pays off over years rather than in a single offer, and that is staying current as the tools change. Data scientists who keep their machine learning and AI fluency sharp protect themselves from the commoditization that hits anyone whose skills stop at last decade's techniques. The discipline of tracking your [earning potential and market value](https://www.metaintro.com/blog/career-metrics-earning-potential-market-value-2026) over time turns these one-off moves into a compounding income curve. The data scientists who out-earn their peers are rarely the most technically brilliant. They are the ones who price their work correctly and ask for the number.

## Is Data Science Still a Good Career Bet in 2026?

Despite a choppy tech hiring market, the long-term demand picture for data scientists remains exceptional. The [Bureau of Labor Statistics](https://www.bls.gov/ooh/math/data-scientists.htm) projects employment of data scientists to grow about 34 percent from 2024 to 2034, several times faster than the average across all occupations, with roughly 23,400 openings expected each year over the decade. That makes data science one of the fastest-growing occupations in the entire economy, and the growth is broad rather than confined to a handful of coastal tech hubs. As more industries treat data as a core asset, the demand spreads into healthcare, logistics, finance, and government, which widens both where the jobs are and which backgrounds can compete for them.

The honest caveat is that the bar to enter has risen. Employers now expect production-grade coding, real machine learning fluency, and the judgment to turn messy data into decisions, not just academic familiarity with models. That raises the entry cost but also protects the pay, because scarce, hard-won skills are exactly what command a premium. If you are still mapping your path in, the route through analytics is the most accessible on-ramp, and our guides on [how to become a data analyst](https://www.metaintro.com/blog/how-to-become-data-analyst-2025), the [essential data analyst skills](https://www.metaintro.com/blog/essential-data-analyst-skills-2025) to build first, and [data analyst portfolio projects](https://www.metaintro.com/blog/data-analyst-portfolio-projects-2025) give you a concrete starting sequence. For your next move, the question is not whether data science pays well, because the data is unambiguous that it does. The question is whether you are positioning your experience, your city, and your skills to capture the top of the range instead of the floor.

---

## Related Articles

- [Software Engineer Salary 2026 by Level and City](https://www.metaintro.com/blog/software-engineer-salary-2026-by-level-and-city)
- [2026 Tech Salary Guide for IT Hiring and Compensation](https://www.metaintro.com/blog/2025-tech-salary-guide-it-hiring-compensation)
- [IT Careers 2026 Salary Guide](https://www.metaintro.com/blog/it-careers-2025-salary-guide)
- [How to Become a Data Analyst Without a Degree](https://www.metaintro.com/blog/become-data-analyst-without-degree)
- [How to Become a Data Analyst](https://www.metaintro.com/blog/how-to-become-data-analyst-2025)
- [Essential Data Analyst Skills](https://www.metaintro.com/blog/essential-data-analyst-skills-2025)
- [How to Negotiate Your Salary for a Higher Offer in 2026](/blog/how-to-negotiate-salary)
- [The AI Skills Salary Premium](https://www.metaintro.com/blog/ai-skills-salary-premium-prompt-engineering-mlops)
- [Data Analyst Portfolio Projects](https://www.metaintro.com/blog/data-analyst-portfolio-projects-2025)
- [Data Analyst Interview Questions](https://www.metaintro.com/blog/data-analyst-interview-questions-2025)
- [Startup Software Engineer Salaries and the Equity Gap](https://www.metaintro.com/blog/startup-software-engineer-salaries-200k-base-pay-25-percent-increase-equity-gap-2026)
- [Could AI Raise Your Salary?](https://www.metaintro.com/blog/could-ai-raise-your-salary-new-study-wage-premium-2026)

---

## People Also Asked

### Q: What is the average data scientist salary in 2026?

A: The U.S. [Bureau of Labor Statistics](https://www.bls.gov/ooh/math/data-scientists.htm) reported a national median wage of $112,590 for data scientists in May 2024, the most recent full-year figure, with the top ten percent earning more than $194,410. Staffing firm [Robert Half](https://www.roberthalf.com/us/en/job-details/data-scientist) puts the 2026 starting range for the candidates it places higher, from about $121,750 to $182,500, with a midpoint near $153,750. Your actual pay depends heavily on experience, city, and industry.

### Q: How much do data scientists make at FAANG companies?

A: Big-tech pay sits far above the national median. According to [Levels.fyi](https://www.levels.fyi/companies/google/salaries/data-scientist), data scientists at [Google](https://www.levels.fyi/companies/google/salaries/data-scientist) earn a median package near $335,000 and up to $895,000 at the most senior level, while at [Meta](https://www.levels.fyi/companies/meta/salaries/data-scientist) the median is near $340,000 and the top of the range exceeds $1.11 million. Most of that upside at senior levels comes from equity, which vests over several years and is not guaranteed in full.

### Q: Which experience level sees the biggest pay jump for data scientists?

A: The largest single increase usually comes when you move from senior to staff or principal. At mainstream employers, senior data scientists often reach the BLS top-decile mark near $194,410, but at a company like [Google](https://www.levels.fyi/t/data-scientist) the median jumps from about $371,045 at the senior L5 level to $445,109 at the staff L6 level. That is why experienced data scientists chase leveling and scope, not just annual raises.

Wondering what you should earn as a data scientist? [Metaintro](https://www.metaintro.com) tracks real compensation data and surfaces roles that match your skills and target pay, so you can negotiate from a position of evidence instead of guesswork. [Sign up for Metaintro](https://www.metaintro.com/signup) to benchmark your number against the market and find data roles that actually pay what you are worth.

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