Data Scientist Salary in Los Angeles — 2026 BLS Data
Salary distribution
Percentile breakdown of Data Scientist base salaries in Los Angeles.
The BLS OEWS May 2024 survey places the median annual base salary for a Data Scientist in the Los Angeles–Long Beach–Anaheim metro at $124,270 — about 10.4% above the national median of $112,590 for the same occupation (SOC 15-2051). That premium sounds reasonable until you remember that Los Angeles costs roughly 49% more to live in than the US average. Adjusted for purchasing power, the LA median actually trails the national figure. This guide unpacks what the headline number hides, how LA compares to other data science hubs, what actually drives the $115,000+ spread between P25 and P90, and how to negotiate a package that reflects the real market rather than the stated midpoint.
What the median hides
The $124,270 median covers an enormous range of situations that have almost nothing in common with each other. A junior data scientist two years out of a master’s program at a healthcare analytics startup lands at a very different number than a staff-level ML practitioner at Netflix or SpaceX with a decade of production modeling experience. The BLS collapses all of that into one occupation code.
The full percentile spread tells the real story. P25 sits at $82,830 — the territory of entry-level roles, smaller companies outside the entertainment and tech industries, and positions with narrowly defined scope (BI-adjacent reporting work relabeled as “data science”). P50 is $124,270. P75 hits $163,900, where you find senior individual contributors at recognizable tech companies and well-capitalized startups. The P90 ceiling is $198,830 on base alone — reached by staff and principal-level scientists at Netflix, SpaceX, Snap, Google’s LA offices, or specialized quant roles in media and entertainment.
That P25-to-P90 span is roughly $116,000 — wider than the entire median salary of most non-tech professions. The practical implication: “data scientist” is not a single job. Where you land in that distribution depends far more on company tier, leveling, and specialty than on raw skill. Understanding those drivers before you negotiate is step zero.
How LA compares to other data science hubs
BLS OEWS May 2024 data and market aggregators paint a consistent picture of where Los Angeles sits in the national hierarchy:
| Metro | P50 Base (BLS / market est.) | COL Index |
|---|---|---|
| San Francisco Bay Area | ~$175,000 | 178.6 |
| Seattle | ~$155,000 | 151.0 |
| New York City | ~$148,000 | 187.2 |
| Los Angeles | $124,270 | 149.0 |
| Austin | ~$118,000 | 119.3 |
| National median | $112,590 | 100.0 |
San Francisco’s premium over LA on base salary looks commanding at face value — about 41% higher. But SF’s COL index of 178.6 versus LA’s 149 means an SF data scientist paying $4,500/month rent is not actually 41% better off than an LA peer paying $2,700. The real purchasing-power gap shrinks to roughly 19% in favor of SF once you normalize for COL. Seattle is the closest comp to LA in both salary and cost structure — it pays about 25% more on base for a roughly equivalent cost of living, largely because of Amazon, Microsoft, and the density of cloud-infrastructure companies concentrated there.
New York carries the highest sticker-price cost of living of any major data science hub, which compresses its real advantage despite the strong nominal salaries. LA, by contrast, has built a distinct niche: a broad mix of entertainment analytics (streaming, studios, gaming), e-commerce (Snap, Dollar Shave Club, The RealReal), aerospace and defense (SpaceX, Northrop, Raytheon), and a growing ML-focused startup ecosystem in Venice and Culver City. The mix produces a median that lags SF and Seattle on cash but with a more livable cost structure than either — and meaningfully below NYC.
What drives the spread: company tier, level, and specialty
Three variables explain almost all of the distance between a $83K P25 offer and a $199K P90 base.
Company tier
The clearest segmentation in the LA data science market is between what practitioners call “Tier 1” companies and everyone else. Netflix, Google (LA/YouTube offices), Amazon, SpaceX, Snap, and a small cluster of well-funded AI startups operate at compensation bands 30–50% above the general LA market. A mid-level (L4 equivalent) data scientist at Netflix or Google LA earns $155,000–$185,000 in base salary. The same experience level at a mid-size media analytics firm, a healthcare company, or a traditional retailer pays $115,000–$135,000. Early-stage startups with under 50 employees often fall in the $95,000–$120,000 range on base and compensate with equity stakes that may or may not materialize.
Financial services and entertainment conglomerates sit in the middle tier. Bank of America’s tech hub downtown, NBCUniversal, Disney’s data teams, and Warner Bros. Discovery all pay respectably — $125,000–$155,000 for senior roles — but without the equity upside that inflates Tier 1 total comp.
Level
Level compression is the single fastest source of salary growth in this market. The jump from a junior title (0–3 years, $82K–$105K base) to a mid-level individual contributor (3–6 years, $115K–$145K) is the biggest single step in absolute dollars. Senior IC (6–10 years, $145K–$175K) is where the P75 number lives. Staff or principal level — typically 10+ years or exceptional technical depth — reaches P90 and above, with base salaries at $175K–$200K-plus at Tier 1 companies.
The catch: leveling criteria at most companies are not posted publicly. The same resume that gets leveled as “senior” at one company gets leveled as “mid-level” at another, which translates directly to a $20K–$40K difference in base. Asking explicitly during recruiting conversations — “What level would this role be calibrated to internally?” — is one of the most valuable questions you can ask before discussing compensation.
Specialty
The LA data science market has developed clear specialty premiums since 2023. NLP and large-language-model roles command the sharpest premium: NLP data scientists in LA average approximately $178,000 annually, roughly 43% above the overall median, driven by demand from streaming companies building recommendation and content intelligence systems and from defense contractors incorporating language AI into document analysis pipelines.
Computer vision and ML infrastructure roles follow at roughly 25–35% above the general median. Recommendation systems, forecasting, and marketing attribution — the bread-and-butter of entertainment and e-commerce analytics — pay closer to median or modestly above. Pure SQL-and-dashboard work branded as “data science” anchors the P25 end of the distribution.
The practical implication: a deliberate investment in production ML skills — deploying models, managing feature stores, evaluating LLM outputs at scale — moves your negotiating range from the middle of the distribution to the top quartile faster than additional years of experience in traditional analytics.
Total compensation breakdown: base, bonus, and equity
BLS OEWS tracks only W-2 wages, which means it captures base salary and cash bonuses but excludes RSU vesting — a significant omission for anyone targeting Tier 1 companies. Market data from Blind and Levels.fyi fills in the equity picture:
Base salary: $124,270 (BLS OEWS P50). This is the number on your offer letter and the number that appears on your W-2. Most companies have formally defined bands per level; recruiters have limited discretion — typically ±8–12% — before needing manager or VP approval to push higher.
Annual bonus: ~$15,000 (roughly 12% of base). LA data science roles vary widely here. Tier 1 tech companies run target bonuses of 10–15% of base paid annually, with payout tied to company and individual performance ratings. Finance-adjacent roles (fintech, asset management) pay higher cash bonuses — 20–30% — but often lower base and less equity. Startups frequently pay no cash bonus and substitute higher base or equity instead.
Annualized equity: ~$40,000. This is where the numbers diverge dramatically from the BLS figure. A mid-level data scientist joining Netflix receives an initial RSU grant of $150,000–$220,000 vesting over four years — roughly $38K–$55K annualized, on top of base. At Google LA the equivalent grant is $120,000–$180,000 over four years. Annual refresh grants start accumulating in year 2 and typically add $20K–$40K in annualized equity at strong performance ratings.
Total: roughly $179,000 at the median, which is consistent with Blind’s reported median total comp of $181,144 for data scientists in Greater LA. That figure is about 44% higher than the BLS-only base — a meaningful gap that matters for any financial planning or offer comparison that mixes companies with and without equity programs.
At P90 total comp (staff-level at a Tier 1 company), packages reach $280K–$350K when equity is included. The Blind P90 for LA data scientists is $335,000. That is the real ceiling, not the $199,000 BLS figure.
Cost-of-living adjusted reality
Los Angeles has a composite cost-of-living index of approximately 149, meaning everyday expenses run 49% above the US national average. Housing is the primary driver: the median rent for a one-bedroom in LA proper runs $2,100–$2,500/month, versus roughly $1,100–$1,300 nationally. Add California state income tax — a 9.3% marginal rate at $124K and 10.3% above $152K — and the take-home difference from comparable salaries in Texas or Washington becomes substantial.
Working the math: a $124,270 LA base has roughly the same purchasing power as $83,400 at the US national average. Put differently, a data scientist earning $90,000 in Indianapolis or $95,000 in Denver is experiencing comparable real purchasing power to an LA colleague at $124,000.
The COL-adjusted picture shifts the hub comparison meaningfully. LA’s $124,270 median delivers less real purchasing power than Austin’s ~$118,000 median (COL index 119.3), because Austin’s lower cost structure more than compensates for the lower nominal salary. Seattle’s $155,000 median at a COL of 151 is roughly equivalent in purchasing power to LA’s median — almost dollar-for-dollar after adjusting. San Francisco’s apparent 41% premium over LA base shrinks to a roughly 19% real advantage once the COL difference is factored in.
This is not an argument against working in LA. The city’s industry mix — entertainment, gaming, aerospace, a maturing tech ecosystem — creates genuine career advantages and network effects that do not show up in a COL calculation. But it does mean that an offer from a remote-first company paying a “national” band of $130,000–$145,000 is not obviously worse than an in-person LA role at $135,000 once taxes and cost of living enter the picture.
Three-lever negotiation playbook
Lever 1: Anchor to specialty and level market rate, not the general median
The $124,270 BLS median is the wrong anchor for most data scientists reading this page. If you have ML deployment experience, NLP skills, or a track record of building production recommendation systems, the defensible market rate is P75 ($163,900) or higher — and that is where you should anchor your first number in a negotiation. Come in with a specific, cited figure: “Based on BLS OEWS data for this metro and Levels.fyi data for this company tier, I’m targeting a base of $160,000–$175,000.” Specificity signals market knowledge, not aggression. Vague asks (“I’m flexible” or “somewhere in the $140s”) invite the company to anchor low.
The general rule: if the recruiter made an offer without significant hesitation after interviews — especially if they used the phrase “we’d love to have you” — you are a strong candidate and they have room to move. Request P75 equivalent for your level. For senior and above roles at Tier 1 companies, P90 base plus full equity is defensible.
Lever 2: Push on signing bonus before touching base bands
Base salary adjustments above the band midpoint require VP or compensation-committee approval at most companies. Equity grants are tied to level guidelines that HR enforces tightly. Signing bonuses are almost always within recruiter discretion up to a stated cap and are refreshed per offer rather than carried on payroll forever — which makes them low-cost to the company and high-value to you in year 1.
If you have a competing offer, use it to justify a signing-bonus request explicitly: “I have an offer from [company] with a $25,000 signing bonus. Can you match that?” You do not need a competing offer to ask; you can simply say, “I have upfront costs with this transition — relocation, unvested equity I’m leaving behind — and I’d like to request a $20,000 signing bonus.” Companies that cannot move base can often find $15,000–$30,000 in signing with minimal friction.
Lever 3: Negotiate your first equity refresh at 18 months
Initial RSU grants vest over four years but most Tier 1 companies issue annual refresh grants beginning in year 2. The first refresh is set based on your level rating at the 12–18 month mark — and the size of that refresh determines your equity trajectory for years 3 and 4. Many data scientists miss this entirely because they never explicitly ask.
At the 15–18 month mark, before your review cycle closes, have a direct conversation with your manager: “I want to make sure my equity refresh reflects both my performance and current market rates. What’s the range for a strong performer at my level?” This signals that you are tracking the number and that you expect a substantive conversation about it. Managers who know a retention risk is building often pull forward a meaningful refresh; those who don’t know will at least be on record as having been asked. A well-timed conversation at this stage routinely moves an annualized equity number by $15,000–$35,000.
Data caveats
BLS OEWS is the most rigorously collected public salary source in the US — it covers tens of millions of workers through a mandatory employer survey rather than self-reported data. But it has structural limitations that matter for data science roles specifically:
Equity is completely excluded. For any role at a company that issues RSUs, BLS understates total annual compensation by 20–50%. The more senior the role and the higher the company tier, the larger the gap. A P90 BLS number of $198,830 and a P90 total-comp reality of $335,000 are both accurate — they are measuring different things.
May 2024 data is now two years old. The AI wave since mid-2024 has pushed compensation for ML-adjacent data science roles significantly higher, particularly in NLP and LLM fine-tuning specialties. By the time you are reading this in 2026, senior ML-focused data scientists at Tier 1 LA companies likely earn 10–20% more than the BLS P90 figure on base alone.
SOC 15-2051 bundles all experience levels. An entry-level analyst with a Python certificate and a staff ML engineer with a decade of production experience appear in the same BLS bucket. The percentile spread is not noise — it reflects genuinely different jobs carrying the same title. Use the percentile data directionally, not as a precise target, and supplement with Levels.fyi company-specific data and California’s mandatory pay-range disclosures on job postings.
For the most accurate current picture: triangulate BLS P50 for base floor, Levels.fyi median total comp for equity-inclusive reality, and California job posting salary ranges for the specific company you are evaluating. The combination gets you within roughly 8–12% of what any given offer should look like before you walk into the negotiation.