Occupation Hub · SOC 15-2051
Data Scientist Salary 2026 — Title Boundary, FAANG Levels, Industry Premium
Clean title boundary table (DS $108K vs DA $84K vs MLE $135-155K vs DE $125K) + 2024-2025 DS market contraction context + level-by-level FAANG comp comparison vs SWE + industry premium analysis showing DS-in-non-tech earns 40-50% of DS-in-tech
TL;DR — Data Scientist Salary
- National median: $108,020 W-2 wage (BLS OES May 2024). P25–P75: $84,990–$152,290; P90 $184,090. Mean $122,910.
- Title boundary matters more than salary range: Data Analyst $84K, Data Scientist $108K, Data Engineer $125K, ML Engineer $135-155K. Same headline, very different jobs.
- Pay gap vs SWE is structural: DS earns 10-25% below comparable SWE at every level, every industry. ML Engineer hybrid role closes the gap.
- Industry premium is huge for DS: Software Publishing $150K vs State Government $90K — 67% spread.
- DS market contracted 2024-2025 as companies consolidated DS work into ML engineering and analytics engineering. Master's still preferred at FAANG-tier.
Data Scientist Salary at a Glance (BLS OES, May 2024)
Data scientists (BLS code 15-2051) are a comparatively young occupational classification — BLS first added the dedicated SOC code in 2018, and the May 2024 release reports about 168,000 employed (substantially smaller than the 1.92M Software Developer count). The annual median wage is $108,020, with mean $122,910. The middle 50% earn $84,990–$152,290; the top 10% exceed $184,090.
The "data scientist" label varies more widely than most occupations. Some companies use it for roles that elsewhere would be called data analyst; others reserve it for ML-modeling specialists. Title boundary alone explains a $50K spread before considering level, industry, or geography.
| Percentile | Annual | Hourly |
|---|---|---|
| P10 | $67,560 | $32.48 |
| P25 | $84,990 | $40.86 |
| P50 (median) | $108,020 | $51.93 |
| P75 | $152,290 | $73.22 |
| P90 | $184,090 | $88.50 |
| Mean | $122,910 | $59.09 |
BLS OES 15-2051, May 2024 release. W-2 wage only; excludes equity grants which lift top-tier total comp 2–3×.
Title Boundary: Data Scientist vs Data Analyst vs ML Engineer vs Data Engineer
Before reading any salary number, you have to know which role it actually describes. The four titles below pay differently and describe different work.
| Title | Median pay (BLS / blended) | Primary work | Tools / skills emphasized |
|---|---|---|---|
| Data Analyst | $83,640 (BLS Statistician/related) | Reporting, dashboards, ad-hoc analysis | SQL, Excel/Sheets, Tableau / Looker / Power BI |
| Data Scientist | $108,020 (BLS 15-2051) | Hypothesis testing, A/B experiments, ML modeling | Python/R, SQL, scikit-learn, statistical reasoning |
| ML Engineer / Applied Scientist | $135K–$155K typical (no dedicated SOC) | Productionizing models, ML infrastructure, MLOps | Python, model serving, distributed training, deployment |
| Data Engineer | $125K (BLS 15-1242 Database Architects) | Building data pipelines, warehouse + lakehouse | SQL, Python, Spark/dbt/Airflow, cloud platforms |
The compounding mismatch: a "Data Scientist" job at a healthcare company is often Data Analyst work at $90K. A "Data Scientist" job at a quant fund is often Quant Researcher work at $300K+. The title alone tells you almost nothing — read the JD's tools, KPIs, and team structure, not the headline.
By Level: Total Comp Ladder
DS levels at FAANG and big tech roughly mirror SWE levels (L3–L7). Total compensation ranges blend Levels.fyi public submissions and recruiter-band data — directional within ±15%.
| Level | Title (typical) | FAANG total comp | Mid-tier tech | Non-tech |
|---|---|---|---|---|
| L3 | DS I / Junior | $160K–$200K | $95K–$135K | $75K–$100K |
| L4 | DS II | $220K–$300K | $130K–$180K | $95K–$130K |
| L5 | Senior DS | $320K–$450K | $180K–$250K | $130K–$170K |
| L6 | Staff DS | $450K–$650K | $250K–$350K | $170K–$220K |
| L7 | Principal DS | $650K–$1M (rare) | $350K–$500K (rare) | — |
Data Scientist pay tracks 10–25% below comparable Software Engineer levels at every company. The gap reflects deeper SWE talent supply, higher revenue attribution per SWE in most P&Ls, and DS roles being more often classified as cost-center vs SWE as profit-center. The recent ML/AI engineering specialty has erased the gap for hybrid roles — but pure DS continues to underearn pure SWE.
By Industry: Tech Premium Is Larger for DS Than for SWE
The "industry premium" matters more for DS than SWE. A senior SWE at a healthcare company earns 60–70% of a senior SWE at FAANG. A senior DS at a healthcare company earns 40–50% of a senior DS at FAANG. If maximum income is the goal, DS-in-tech is the only path that scales.
| Industry | DS mean wage | Note |
|---|---|---|
| Software Publishing | $150,210 | FAANG, large SaaS — pure tech DS |
| Management of Companies | $135,470 | Holding-co data teams; finance industry overlap |
| Insurance Carriers | $130,030 | Actuarial-adjacent DS; growing investment area |
| Pharma R&D | $128,880 | Biostatistics overlap; PhD common |
| Banks & Securities | $125,290 | Credit-risk + fraud + algo trading proximity |
| Federal Government | $118,540 | Capped GS scale + locality pay |
| Hospitals | $104,930 | Healthcare DS; weak ceiling vs tech |
| Educational Services | $92,180 | University DS; ed-tech |
| State + Local Government | $89,560 | State analytics; pension-stabilized |
Education Path: Master's Still Preferred at FAANG-Tier
DS is more credential-heavy than SWE — the master's degree expectation has not loosened the way it has for SWE.
- Stats / CS / Quantitative master's — standard preferred entry. Common programs: NYU MS Data Science, Stanford ICME, Berkeley MIDS, CMU MISM. Total cost $30K–$90K, 12–24 months.
- PhD (Stats, CS, Physics, Econ, Operations Research) — common at FAANG and quant. Pay premium typical (15–30% above master's-only at same level). PhD tax: 4–6 years of foregone earnings.
- Bachelor's only — possible at non-tech industries (healthcare, retail, gov), tougher at FAANG without strong portfolio (Kaggle, papers, open-source).
- Bootcamp — DS bootcamp placement is much weaker than SWE bootcamp placement in 2026. The DS market re-tightened in 2024–2025 and bootcamp-trained DS faces stronger hiring filters.
- Self-taught + portfolio — works if portfolio is exceptional (research papers, novel Kaggle-grade work, open-source contribution to major DS libraries).
The 2024–2025 contraction: The 2020–2023 DS hiring boom retracted as companies consolidated DS work into ML engineering and analytics engineering. Pure DS roles still exist but concentrate at well-funded tech and traditional analytics-rich industries (insurance, pharma). New entrants face stronger filtering than 5 years ago. AI/ML engineering subspecialty is the hottest sub-niche in 2026.
Switching to / from Data Science
DS sits in the middle of a cluster of adjacent roles. Common pivot patterns:
- SWE → DS: 12–18 months part-time prep + internal switch. SWEs already have engineering hygiene; the gap is statistical reasoning and stakeholder communication.
- DS → SWE: 18–24 months part-time prep + lateral move to ML engineering before full SWE. DSs often lack production-systems and infra depth.
- DA → DS: typical career progression at most companies; usually 2–4 years of strong analyst work + learning Python/ML before promotion or external move.
- DS → ML Engineer: the natural specialization path 2024+. ML Engineer pay matches SWE bands; for many DSs, this is the highest-leverage move.
- Quant Researcher → DS: common in finance-to-tech moves. Quant compensation usually exceeds DS at same level, so this is a lifestyle/culture move, not a pay move.
- PhD academic → industry DS: stable pipeline; PhDs in math, physics, biostatistics, economics often clear FAANG DS level interviews directly.
See SWE vs DS comparison for full task-level differences and switching cost analysis.
Methodology & Data Sources
Wage data: BLS OES 15-2051 (Data Scientists), May 2024 release. Adjacent codes: 15-2041 (Statisticians), 15-1242 (Database Architects), 15-2031 (Operations Research Analysts). Industry-segmented: BLS OES NAICS-coded files. Total comp ranges: Levels.fyi public submissions 2024–2025 + Blind threads + recruiter-published bands. Real-wage adjustment: BEA Regional Price Parities (2024). Bootcamp + master's program placement context: CIRR reports + university-published employment outcomes. Glassdoor / Indeed wage aggregations systematically inflate by 8–18% vs BLS — when figures diverge, BLS OES is authoritative for W-2 comparison.
FAQ
- What is the average data scientist salary in 2026?
- Per BLS OES May 2024, the national annual median wage for data scientists (15-2051) is $108,020 with a mean of $122,910. P25–P75: $84,990–$152,290; P90 $184,090. This is W-2 wage only. At FAANG-tier companies, total comp (base + RSU vests + bonus) typically runs 1.5–3× the BLS median for senior+ levels.
- How much does a senior data scientist make at FAANG?
- L5 Senior DS total comp at FAANG is typically $320K–$450K. L6 Staff DS: $450K–$650K. L7 Principal DS: $650K–$1M (rare; pay caps below SWE-equivalent levels at most companies). Non-tech senior DS: $130K–$170K. Sources: Levels.fyi 2024–2025 aggregations, treated as directional within ±15%.
- Is data science still a good career in 2026?
- Yes, but with caveats. The 2020–2023 DS hiring boom retracted as companies consolidated DS work into ML engineering. Pure DS roles still exist and pay well — they just cluster more at well-funded tech and traditional analytics-rich industries (insurance, pharma, retail). New entrants face stronger filtering than 5 years ago. AI/ML engineering subspecialty is the hottest growth area; pure DS is steady but less rapidly expanding.
- Do data scientists earn more than data analysts?
- Yes — by ~$25–35K typically. Data Analyst median ~$83,640 (BLS Statisticians/related); Data Scientist median $108,020. The role distinction is real: Analysts focus on SQL/dashboards/ad-hoc reporting; Scientists do hypothesis testing, A/B experiments, and ML modeling. Promotion DA→DS is the standard career progression at most companies, typically 2–4 years of strong analyst work + Python/ML upskilling before the move.
- Data Scientist vs ML Engineer — which pays more?
- ML Engineer pays more, by 10–25% at most companies. ML Engineer (sometimes 'Applied Scientist') typical median $135–155K vs DS $108K. ML Engineer pay tracks SWE bands at FAANG; the ML specialty often commands 5–15% above general SWE. For DSs whose work is heavy on productionization and modeling, transitioning to ML Engineer is the highest-leverage move available.
- What master's degrees lead to data science?
- Stats, CS, and Quantitative master's are the standard preferred entries. Common programs: NYU MS Data Science, Stanford ICME, Berkeley MIDS, CMU MISM, Georgia Tech OMSCS (online, $7K total). Total cost typically $30K–$90K, 12–24 months. Bachelor's-only DS is possible at non-tech (healthcare, gov, retail), tougher at FAANG without master's or strong portfolio (papers, Kaggle, open-source). PhDs (Stats, CS, Physics, Econ, OR) command 15–30% pay premium at same level.
- Are bootcamp data scientists employable in 2026?
- Much harder than 2018–2021. The DS market re-tightened in 2024–2025 and bootcamp-trained DS faces stronger hiring filters than bootcamp SWE. CIRR-reporting bootcamps publish placement rates but the job-finding timeline has lengthened. Bootcamp DS candidates often have better luck targeting Data Analyst entry, then promoting to DS internally. Top CIRR DS bootcamps: Bloom Tech (formerly Lambda School), Springboard, Metis (now closed), General Assembly.
- Which industry pays data scientists the most?
- Software Publishing ($150,210 mean) leads. Followed by Management of Companies ($135,470), Insurance Carriers ($130,030), Pharma R&D ($128,880), Banks & Securities ($125,290). Lowest: State + Local Government ($89,560), Educational Services ($92,180), Hospitals ($104,930). The DS-in-tech premium is much larger than the SWE-in-tech premium — non-tech DS earns 40–50% of tech DS at the same level. If maximum income is the goal, DS-in-tech is the only DS path that scales.
- What about quant researcher / quant developer roles?
- Quant roles in finance (hedge funds, trading firms) typically pay more than DS at the same level — often $300K–$700K+ at senior. Quant Researcher and Quant Developer have stricter hiring filters (math/stats/physics PhD, sometimes finance-specific certifications) but compensate accordingly. Quants → DS pivots are common when senior quants want lifestyle change; pay typically drops 30–50% on the move. DS → Quant is rare and difficult unless you have a strong math background.
- How does data science differ from machine learning engineer day to day?
- DS day: 30–40% data wrangling, 20–30% exploratory analysis, 15% modeling/experimentation, 15% communication (writeups, presentations), 12% meetings, 5% productionization. ML Engineer day: 50–60% production code (model serving, infrastructure, MLOps), 15% modeling, 10% review, 10% meetings, 10% documentation/runbooks. ML Engineer is more SWE-like; DS is more analyst+stats-like. Pay reflects this: ML Engineer compensates closer to SWE, pure DS compensates lower.