Data Scientist Salary in California: Pay by Experience, City & Take-Home

A data scientist in California earns a median of $141,590 a year, according to the U.S. Bureau of Labor Statistics (2025). That works out to about $68.07 an hour. Most of the field falls between $77,480 at the 10th percentile and $224,920 at the 90th, so where you land depends heavily on your metro, your experience, and the kind of company writing the check.

How California pay compares to the national figure

The national median for data scientists is $120,230, per BLS (2025). California’s median of $141,590 sits $21,360 above that, a premium of roughly 18 percent. That gap is real, but so is the cost of living behind it, which is why the take-home section further down matters more than the headline.

The spread inside California is wide. A data scientist at the 10th percentile earns $77,480, while someone at the 90th clears $224,920. That’s a difference of more than $147,000 between the bottom and top of the same job title in the same state. The middle of the market, the 25th to 75th percentile, runs from $103,360 to $186,820. California employs about 39,310 data scientists, one of the largest concentrations of the role in the country, which keeps demand and pay both high.

Data scientist salary by experience in California

BLS reports pay by percentile rather than by years on the job, but the percentiles map cleanly onto career stage. Entry-level analysts and new grads cluster near the 10th and 25th percentiles. Mid-career people sit around the median. Senior and staff-level scientists, the ones owning model pipelines or leading teams, push into the 75th and 90th. Here is how that looks in dollars.

The jump from early career to mid-career is the steepest single step here, about $38,000 between the 25th percentile and the median. That reflects how quickly a data scientist’s value climbs once they can ship production models, communicate findings to non-technical leaders, and own a problem end to end rather than just running assigned queries. The gap from experienced to senior, $186,820 to $224,920, is another $38,100, and that one is usually earned through scope: leading a team, setting modeling strategy, or specializing in a high-demand area like machine learning infrastructure.

Put the steps side by side and a pattern emerges. From entry ($77,480) to early career ($103,360) is a $25,880 lift, usually the payoff for your first one to two years of shipped work. From early career to the median is $38,230. From median to experienced is $45,230, the single largest dollar step in the table, and from experienced to senior is $38,100. So the biggest absolute raise in a California data scientist’s career tends to land in the move from solidly mid-career to genuinely experienced, the stretch where you go from executing well-defined tasks to defining what the team should work on. Knowing where that step sits helps you time a job change: the market rewards the transition into ownership more than any other single move.

One caution on reading these numbers. The percentiles are a snapshot of everyone holding the title at once, not a guaranteed escalator for any one person. A data scientist who stays in a narrow reporting role can sit near the median for years, while someone who deliberately takes on modeling strategy and stakeholder-facing work can cross from the 50th to the 75th percentile in two or three years. The dollars are real, but the timeline is yours to set.

Data scientist salary by California metro

Location inside California swings pay more than almost any other factor. The Bay Area tech corridor pays a different planet’s wages from inland California. These are the five largest metros for the role by employment, with BLS (2025) median figures.

San Jose tops the state at $185,080, which is $81,380 more than Sacramento’s $103,700. That is the single starkest pay cliff in California for this job, and it tracks the concentration of large tech employers in Silicon Valley. San Francisco follows at $170,110 and carries the most jobs of any metro, 10,460, so it is both the deepest market and one of the best paid. Los Angeles is the second-largest job market at 9,850 but pays a more grounded $129,740, close to San Diego’s $130,990. Sacramento, anchored more by state government and healthcare than by venture-backed tech, sits at the bottom of the five at $103,700, which is still above the national 25th-percentile range for the role. If you can work remotely for a Bay Area employer while living in Sacramento, you capture much of the upside without the housing cost, which is the move a lot of California data scientists now make.

Look closer at the two Bay Area metros, because they behave differently. San Jose has the higher median, $185,080, but a smaller pool of 6,060 jobs, which fits a market dominated by a handful of very large, very high-paying technology and hardware employers. San Francisco’s $170,110 comes with nearly twice the job count, 10,460, spread across a broader mix of software, finance, and biotech firms. That breadth matters when you are job hunting: San Francisco gives you more shots on goal, while San Jose gives you a higher ceiling if you can break in. Together the two metros account for 16,520 of California’s 39,310 data scientist jobs, so just over 40 percent of the entire state’s data science workforce sits in the greater Bay Area.

The Southern California picture is its own market. Los Angeles ($129,740) and San Diego ($130,990) are within about $1,250 of each other, and together they hold 12,680 jobs, almost a third of the state total. Pay there is roughly $40,000 to $55,000 below the Bay Area median depending on which pair you compare, but housing is meaningfully cheaper, especially east of the coast, so the lower gross does not translate one for one into a lower standard of living. San Diego in particular blends defense, biotech, and a growing startup scene, which keeps its median a hair above Los Angeles despite a much smaller headcount of 2,830. For anyone weighing an offer, the practical takeaway is that the four coastal metros, San Jose, San Francisco, Los Angeles, and San Diego, hold the overwhelming majority of well-paid California data science work, while Sacramento functions as the affordable-living option you can pair with a remote coastal employer.

Take-home pay after tax in California

California taxes income, and at data-scientist salaries the marginal state rate climbs fast. Here is what each career stage actually keeps after federal income tax, FICA (Social Security and Medicare), and California state income tax, for tax year 2026. Net monthly is what hits your account before any 401(k), health premiums, or other deductions.

The effective tax rate rises from 23.0 percent at entry level to 30.5 percent at the median and 33.6 percent at the senior level. So a median data scientist grossing $141,590 nets $98,335 a year, or $8,195 a month. The state portion alone at the median is $9,195, which is the piece a no-tax state would hand back to you.

Watch how the burden shifts as pay rises. At entry level, federal tax ($8,660), FICA ($5,927), and California state tax ($3,233) are fairly close in size. By the senior level, federal tax ($44,237) dwarfs the other two, while FICA flattens out at $14,404 because the Social Security portion stops applying above the annual wage cap. That flattening is why the effective rate climbs only from 32.7 percent at the experienced level to 33.6 percent at senior, even though gross pay jumps $38,100: a chunk of that raise dodges the Social Security tax. The state tax line, meanwhile, more than doubles from entry to median ($3,233 to $9,195) and nearly doubles again to senior ($16,945), because California’s brackets are steeply progressive at these incomes. The more you earn here, the larger the share California claims relative to a flat or no-tax state.

Here is the comparison that matters most. A California data scientist at the state median nets $98,335 after tax. The same $141,590 salary in Texas, which has no state income tax, nets $107,530. That is a gap of $9,195 a year that California keeps and Texas does not. Over a decade, ignoring raises, that is more than $90,000 in lifetime take-home difference driven purely by state tax. It does not mean Texas wins. It means you have to weigh that $9,195 against what the California job actually pays in gross, since Bay Area median pay can be tens of thousands higher than a comparable Texas role.

The number that actually governs your life is net pay minus local housing. A San Jose data scientist nets more in raw dollars than almost anyone in the field, but median Bay Area rent eats a large share of that net before anything else. A Sacramento or San Diego scientist keeps less gross but often more discretionary income after the mortgage. Run your own figure rather than trusting the headline. Plug your exact salary into the California paycheck calculator to see your real monthly net, and if you want help squeezing the most out of deductions at these income levels, tax software like TurboTax walks through California-specific credits and itemizing.

ThePayGuide may earn a commission from links on this page at no cost to you. See our editorial policy.

How to earn more as a data scientist in California

The percentile spread shows there is real room to move up without leaving the state. Concrete levers:

• Target the Bay Area, even remotely. San Jose pays $185,080 at the median and San Francisco $170,110, against $103,700 in Sacramento. Landing a Bay Area employer is the fastest single raise available, and remote arrangements let you keep more of it.
• Specialize in machine learning engineering. Scientists who can deploy and maintain models in production, not just prototype them, sit in the 75th to 90th percentile range, $186,820 to $224,920. The deployment skill set is where the pay concentrates.
• Build a credential that proves the production skills. Cloud machine learning certifications (AWS, Google Cloud, Azure) and structured programs in MLOps move you up the percentile ladder faster than another generic analytics course. Platforms like Coursera carry employer-recognized certificates in deep learning, ML engineering, and cloud data platforms.
• Move toward scope, not just skills. The step from experienced to senior is worth about $38,100. It usually comes from owning a problem area, mentoring, or leading a small team, so take the project nobody else wants to own.
• Negotiate on equity, not just base. At venture-backed California employers, equity and bonus can rival base pay. BLS figures are base wages, so the real total comp at the top end runs higher than the $224,920 90th-percentile number suggests.

Two of these levers compound. If you specialize in production machine learning and you do it for a Bay Area employer, you are stacking the metro premium ($185,080 San Jose median against the $141,590 state median, a $43,490 gap) on top of the specialization premium that pushes you toward the 75th or 90th percentile. That combination is how California data scientists reach the $186,820 to $224,920 band. The single most common mistake is chasing the title without the scope: someone who collects certifications but never owns a production system tends to stall near the median, because the market pays for shipped, maintained work, not for credentials on their own. Use the credential to open the door, then make sure the job behind it actually puts you on the deployment side of the house.

It is also worth being deliberate about timing. The biggest dollar step in the experience table, the $45,230 move from the median to the experienced 75th percentile, usually requires a job change rather than an internal raise, because internal pay bands tend to compress. If you have hit a ceiling at your current employer, the live California market is deep enough that switching is realistic, and a switch is often where the percentile jump actually happens.

Job outlook for data scientists in California

California already employs about 39,310 data scientists, per BLS (2025), among the largest pools of the role anywhere in the country. The state’s concentration of technology, biotech, entertainment, and finance employers keeps demand steady, and data science remains one of the faster-growing occupations nationally. The depth of the San Francisco market, 10,460 jobs, and Los Angeles, 9,850, means there is genuine liquidity: you can change employers without changing cities. If you want to gauge current openings and pay in your metro, scanning live listings on a board like ZipRecruiter gives a real-time read that complements the annual BLS baseline.

Related and higher-paying roles in California

It helps to see where the data scientist title sits against neighboring roles in the same state. Using BLS (2025) California medians, a Software Developer earns $174,410, which is $32,820 MORE than a data scientist in California. A Computer Systems Analyst earns $127,720, which is $13,870 LESS. So the software developer track is the clear pay step up if you have the engineering chops, while systems analysis trades some pay for, often, a steadier and less research-heavy day.

Frequently asked questions

Sources: U.S. Bureau of Labor Statistics, OEWS May 2025 (occupation 15-2051, Data Scientists); ThePayGuide tax modeling for 2026. Last updated 2026. See our methodology.