The average data scientist salary in the US sits around $152,000 in 2026, but that number hides enormous variation. A junior data scientist in a mid-market company might earn $95,000, while a staff-level data scientist at a FAANG company can pull in $350,000+ when you factor in equity. If you’re benchmarking your compensation or planning a career move, the averages rarely tell the full story.
I’ve spent years working alongside data science teams and reviewing compensation data across dozens of companies. Here’s what the data scientist salary picture actually looks like in the US right now, broken down by the factors that matter most.
Data Scientist Salary in the US by Experience Level
Experience is the single biggest lever on data scientist compensation. The jumps between levels are significant, especially once you cross into senior territory.
| Experience Level | Base Salary Range | Total Compensation (with bonus/equity) |
|---|---|---|
| Entry-Level (0-2 years) | $85,000 – $115,000 | $90,000 – $130,000 |
| Mid-Level (3-5 years) | $120,000 – $160,000 | $140,000 – $200,000 |
| Senior (6-9 years) | $155,000 – $195,000 | $185,000 – $280,000 |
| Staff/Principal (10+ years) | $190,000 – $240,000 | $250,000 – $400,000 |
| Director/Head of DS | $210,000 – $280,000 | $300,000 – $500,000+ |
The gap between base salary and total compensation widens dramatically at senior levels. At FAANG and top-tier tech companies, equity can represent 40-60% of total compensation for staff-level roles. This is why base salary comparisons alone are misleading.
Data Scientist Salary Differences by City and Region
Geography still matters, even with remote work reshaping the market. Companies increasingly use location-based pay bands, and the differences are substantial.
Top-Paying US Cities for Data Scientists
| City | Average Base Salary | Cost of Living Index | Adjusted Salary |
|---|---|---|---|
| San Francisco, CA | $178,000 | 180 | $98,900 |
| New York, NY | $168,000 | 170 | $98,800 |
| Seattle, WA | $170,000 | 160 | $106,250 |
| Austin, TX | $145,000 | 110 | $131,800 |
| Boston, MA | $158,000 | 145 | $108,960 |
| Denver, CO | $140,000 | 115 | $121,740 |
| Chicago, IL | $138,000 | 110 | $125,450 |
| Raleigh-Durham, NC | $132,000 | 100 | $132,000 |
When you adjust for cost of living, cities like Austin, Denver, and Raleigh-Durham actually offer better purchasing power than San Francisco or New York. This is a big reason why data scientists have been migrating to these markets since the remote work shift accelerated in 2020.
Remote Data Scientist Pay
About 65% of data scientist roles now offer some form of remote or hybrid work. Most large tech companies apply geographic pay adjustments of 10-25% for employees outside of tier-one metro areas. Some companies, like Gitlab and Automattic, use transparent location-based formulas. Others simply set a national band that’s lower than their SF/NYC rates but higher than local market averages.
If you’re negotiating a remote offer, ask specifically about the company’s location adjustment policy. The difference between “we pay SF rates everywhere” and “we adjust by 20% for non-hub locations” can mean $30,000 or more annually.
How Industry Affects Data Scientist Compensation
Not all data science jobs are created equal from a compensation standpoint. The industry you’re in can shift your salary by 30% or more, even for identical skill sets.
| Industry | Average Base Salary | Notes |
|---|---|---|
| Big Tech (FAANG+) | $175,000 – $220,000 | Massive equity packages push total comp much higher |
| Finance/Fintech | $160,000 – $200,000 | Strong bonuses (20-50% of base), especially at hedge funds |
| Healthcare/Pharma | $135,000 – $175,000 | Growing fast with AI/ML adoption in drug discovery |
| Retail/E-commerce | $130,000 – $165,000 | Amazon and Walmart compete with tech salaries |
| Consulting | $120,000 – $155,000 | Lower base but rapid promotion tracks |
| Government/Non-profit | $90,000 – $125,000 | Better benefits and job security offset lower pay |
| Startups (Series A-C) | $130,000 – $170,000 | Equity is the wild card: could be worth millions or zero |
Quantitative hedge funds remain the outliers. Firms like Two Sigma, Citadel, and DE Shaw routinely pay $300,000-$500,000+ in total compensation for experienced data scientists. The catch: the work is intense, the hiring bar is extremely high, and the culture isn’t for everyone.
Skills That Command Higher Data Scientist Salaries
The “data scientist” title covers a wide range of actual work. Certain specializations consistently command a premium.
High-Value Technical Skills
- MLOps and model deployment: Data scientists who can put models into production (not just build them in notebooks) earn 15-20% more than those who can’t. Companies are tired of models that never ship.
- LLM fine-tuning and prompt engineering: In 2026, experience with large language models is the hottest skill in data science. Roles focused on LLM applications are paying $20,000-$40,000 premiums over general data science positions.
- Causal inference: Especially valued in tech (A/B testing at scale) and healthcare. This is harder to learn than prediction modeling and employers know it.
- Deep learning/computer vision: Still commands a premium in manufacturing, autonomous vehicles, and medical imaging.
Non-Technical Skills That Pay
Technical chops alone won’t get you to the top of the pay band. Data scientists who can translate findings for business stakeholders, scope their own projects, and influence product decisions consistently out-earn their purely technical peers. If you’re looking to grow in this direction, investing in your data science career path strategically matters more than stacking certifications.
Data Scientist Salary Negotiation: What Actually Works
Most data scientists leave money on the table during negotiation, especially early in their careers. Here’s what I’ve seen work repeatedly.
Know your number before the conversation starts. Use Levels.fyi for tech companies (it’s the most accurate source for total compensation data), Glassdoor and Blind for cross-industry ranges, and your own network for sanity checks. The Bureau of Labor Statistics reports a median of $108,020 for “data scientists” but this figure significantly underestimates actual tech market rates.
Negotiate total compensation, not just base salary. Many companies have rigid base salary bands but flexibility on signing bonuses, equity grants, and annual bonus targets. A $15,000 signing bonus and an extra $20,000 in RSUs can be easier to secure than a $10,000 base salary increase.
Time your move carefully. The strongest negotiating position comes from having a competing offer. Even if you prefer one company, having alternatives gives you concrete data points to reference. “I have an offer at $X” is far more effective than “I think I’m worth $X.”
How Data Scientist Salaries Compare to Related Roles
Understanding where data science sits in the broader compensation landscape helps with career planning.
| Role | Average US Base Salary | Comparison |
|---|---|---|
| Data Scientist | $152,000 | Baseline |
| Machine Learning Engineer | $165,000 | +8.5% |
| Data Engineer | $148,000 | -2.6% |
| Data Analyst | $95,000 | -37.5% |
| Software Engineer | $155,000 | +2% |
| AI/ML Research Scientist | $185,000 | +21.7% |
Machine learning engineers and AI research scientists typically out-earn general data scientists because these roles require deeper technical specialization. If maximizing compensation is your priority and you have the skills, pivoting toward ML engineering or applied research can be worth considering. For a detailed comparison, see our guide to the best data science programs that can help you build the right skills.
Frequently Asked Questions
What is the starting salary for a data scientist in the US?
Entry-level data scientists with 0-2 years of experience typically earn between $85,000 and $115,000 in base salary. Total compensation (including bonuses and equity) ranges from $90,000 to $130,000. Graduates from top programs or those with strong internship experience at tech companies tend to land at the higher end of this range. Location matters significantly: an entry-level role in San Francisco might start at $110,000+ while the same role in a mid-tier city might start around $90,000.
Is data science still a high-paying career in 2026?
Yes, data science remains one of the highest-paying non-management technical careers in the US. While the explosive growth in job postings has normalized compared to 2019-2022, demand for experienced data scientists continues to outstrip supply. The integration of AI and LLMs into business operations has created new specializations within data science that command even higher premiums. The US Bureau of Labor Statistics projects 35% job growth for data scientists through 2032, well above the average for all occupations.
Do data scientists earn more than software engineers?
At the median level, data scientists and software engineers earn roughly similar base salaries (within 2-3% of each other). The difference shows up at the extremes: top software engineers at FAANG companies can earn more through well-established career ladders (L7+ at Google, for example), while data scientists at quantitative finance firms can out-earn almost everyone in tech. The more meaningful comparison is total compensation, which depends heavily on company, level, and equity structure rather than job title alone.
How can I increase my data scientist salary quickly?
The three fastest paths to a meaningful salary increase are: (1) changing companies, which typically yields a 15-25% bump compared to 3-8% from internal promotions, (2) building production ML and LLM skills that are in high demand right now, and (3) moving into a specialization like ML engineering or applied AI research. Certifications alone rarely move the needle on compensation, but demonstrating real project impact does. Focus on shipping work that generates measurable business value, then make sure you can articulate that impact clearly during interviews.
Ben is a full-time data leadership professional and a part-time blogger.
When he’s not writing articles for Data Driven Daily, Ben is a Head of Data Strategy at a large financial institution.
He has over 14 years’ experience in Banking and Financial Services, during which he has led large data engineering and business intelligence teams, managed cloud migration programs, and spearheaded regulatory change initiatives.