Data Scientist Salary in 2026: Cities, Levels, and What Drives the Top of the Range

The Data Scientist Pay Distribution

The BLS lumps data scientists under occupation code 15-2051 with a 2026 national median of $138,000. But that median masks the most-bimodal salary distribution of any modern tech role: junior generalist data scientists at non-tech companies start around $90K, while senior ML engineers at FAANG can clear $500K total compensation.

The split has two drivers: (1) which industry you work in, and (2) how deep your specialization is between "analytics" and "ML engineering."

National Data Scientist Salaries by Level

• Junior (0–2 years): $90K–$135K base
• Mid (3–5 years): $135K–$185K base
• Senior (6–9 years): $185K–$240K base
• Staff / Principal (10+ years): $240K–$340K base

These are base-salary ranges. Add equity at FAANG-tier employers, which often doubles total compensation at senior level and above.

City-Level Median Data Scientist Salary

• San Francisco / San Jose: $182K
• Seattle: $172K
• New York City: $165K
• Boston: $158K
• Austin: $148K
• Denver: $138K
• Chicago: $138K
• Los Angeles: $148K
• Atlanta: $128K
• Washington DC: $148K
• Raleigh: $128K
• Phoenix: $122K
• Dallas: $132K
• Salt Lake City: $125K

The SF / Seattle / NYC tier sits well above the rest because that's where the FAANG and major fintech / hedge fund employers concentrate.

The Tech vs. Non-Tech Split

Tech employers (FAANG, mid-tier tech, well-funded startups):

• Junior: $130K–$170K base, often $30K–$80K in initial equity grant.
• Senior: $215K–$280K base, plus $150K–$400K annual equity.
• Staff: $280K–$385K base, plus $400K–$900K annual equity.

These numbers reflect what Levels.fyi consistently shows for self-reported FAANG offers.

Non-tech employers (banks, insurance, consulting, healthcare, government):

• Junior: $85K–$120K, no equity.
• Senior: $145K–$200K, modest bonus (5–20%), no equity.
• Staff equivalents: $185K–$250K, larger bonus (15–30%), no equity.

The non-tech ceiling is $250K–$350K total comp at very senior levels (Director-of-Data-Science roles). Tech ceiling is open-ended above $500K.

Specialization Premiums

ML / AI engineering (model architecture, deployment, MLOps): 25–40% premium over generalist data scientist of equivalent level. This is the highest-paid specialization in 2026.

Computer vision, NLP, deep learning specialists: 30–50% premium at senior+ level. Demand vastly exceeds supply.

Causal inference / experimentation: Highly valued at big tech (especially marketplace and ads companies). 15–25% premium.

Time-series forecasting: Specialized but more abundant. 5–15% premium in finance and supply chain contexts.

Generalist analytics: Baseline. No premium. Often the starting point for data careers; many people exit to product analytics or BI engineering roles.

Education Requirements

The market in 2026 looks like this:

• Bachelor's degree only: 18% of data science roles. Mostly junior analytics positions or self-taught specialists with strong portfolios.
• Master's degree: 56% of roles. The dominant credential.
• PhD: 26% of roles. Required for most ML research positions, biotech, and academic-adjacent industries.

A PhD is not necessary for the highest-paying tech ML engineering roles — many staff-level ML engineers at FAANG hold only a Bachelor's or Master's degree with strong applied experience.

YoY Growth

BLS projects 35% job growth for data scientists from 2022 to 2032 — faster than almost any other professional occupation. Demand is concentrated in:

• Healthcare and pharmaceutical analytics
• Financial services (fraud detection, credit scoring, algorithmic trading)
• E-commerce (recommendation systems, A/B testing)
• Generative AI (model evaluation, RLHF, prompt engineering)

How to Move Up the Pay Bands

Junior → Mid (12–24 months): Ship one production model end-to-end. Document your impact in dollars (revenue lift, cost reduction). Apply externally; most $30K+ jumps happen by switching companies, not internal promotions.

Mid → Senior (3–4 years total): Lead a project, not just contribute. Mentor a junior. Pick a vertical specialization (NLP, recommender systems, etc.) — generalists plateau at mid-level.

Senior → Staff (6–8 years total): Operate at organizational level — propose initiatives across teams, write technical strategy docs, set hiring bars. Staff-level is as much about influence as code.

Anyone → FAANG: Even one year inside a top tech company resets your pay band by 50–80%. Many data scientists do 2–3 years at FAANG, then leverage the credential into senior roles elsewhere at much higher pay than they would have earned organically.

Bottom Line

The data scientist title hides enormous variance. A generalist analytics data scientist at a bank earns substantially less than an ML engineer at OpenAI with the same years of experience. The single biggest career decision in this field is which side of that line you optimize for — the path you choose at year 2 largely determines your year-10 compensation.

Browse data scientist salaries by city in our [salaries directory](/salaries/data-scientist/). Submit your own data scientist salary [here](/salary-submit/?role=data-scientist).