Data Scientist Salary in USA 2025
Pay by experience level, state, and industry
Median Total Compensation (USA)
Data Scientist Salary Overview
This guide breaks down data scientist salary in the US for 2025, covering average and median pay, starting salaries, senior compensation, and how earnings vary by location and industry. Whether you're evaluating a job offer, planning a career transition, or negotiating a raise, these figures provide the benchmarks you need.
Data science remains one of the highest paying jobs in the US, with strong demand across tech, finance, healthcare, retail, and virtually every industry collecting data. The Bureau of Labor Statistics projects data scientist roles to grow much faster than average, and compensation continues to reflect this demand.
The data here draws from government sources (BLS), large industry salary databases, and compensation aggregators, presented in realistic ranges so you can compare offers and plan your career. For broader context on how data science fits within tech salaries, see our industry comparison guide.
What Data Scientists Earn: Salary Snapshot
Data scientist compensation in the US typically falls in the low-to-mid six-figure range for total pay (base salary plus bonuses and sometimes stock). Recent data sources place median total compensation around $110,000–$140,000 for mid-level roles, with significant variation based on experience, location, and industry.
Entry-level positions start lower but still well above most other professions—often in or near six figures. Senior and lead data scientists, particularly those at major tech companies or in specialized AI roles, can earn well into multiple six figures when stock and bonuses are included.
| Level | Typical Total Annual Pay (USD) | Notes |
|---|---|---|
| Entry-Level (0–2 years) | ~$90,000–$115,000 | Strong starting pay vs. most fields; varies by market |
| Mid-Level (3–6 years) | ~$120,000–$160,000 | Common range across multiple datasets |
| Senior / Lead | ~$160,000–$200,000+ | Higher responsibility, specialized skills |
| Principal / Staff | ~$200,000–$300,000+ | Technical leadership, strategy roles |
| Big Tech / FAANG | Can exceed $350,000+ | Salary + bonus + significant stock grants |
These figures represent approximate ranges from multiple sources. Actual compensation varies by methodology and source, so treat them as realistic benchmarks rather than precise promises.
Data Scientist Salary by Experience Level
Entry-Level / Junior Data Scientist
New data scientists with 0–2 years of experience typically earn $90,000–$115,000, depending on location, company type, and educational background. This starting range already exceeds most other entry-level professional salaries, reflecting the specialized skills required for the role.
Factors that push entry-level pay toward the higher end include: advanced degrees (Master's or PhD), relevant internships or prior analytics experience, strong programming skills (Python, SQL, R), and landing a role at a well-funded tech company or in a major market like San Francisco or New York.
Mid-Level Data Scientist (3–6 years)
After a few years of experience, data scientists who can own end-to-end projects—from problem framing through model deployment and stakeholder communication—typically see significant salary jumps. Mid-level data scientists commonly earn $120,000–$160,000 in total compensation.
This is often where career acceleration happens. Data scientists who develop expertise in high-demand areas (deep learning, NLP, recommendation systems, causal inference) or who move into high-paying industries can reach the upper end of this range or beyond.
Senior, Lead, and Principal Data Scientist
Senior data scientists (6+ years) typically earn $160,000–$200,000+ in total compensation. Lead and principal roles—requiring strategic thinking, cross-functional leadership, and influence on technical direction—can earn $200,000–$300,000 or more.
At major tech companies, senior and staff data scientist compensation often includes substantial stock grants. A Staff Data Scientist at Google or Meta might have a $180,000 base salary but $350,000+ in total compensation once stock and bonuses are included.
Reaching these levels requires demonstrated impact: building models that drive significant business outcomes, leading data strategy, mentoring junior team members, and influencing product decisions. For strategies on advancing your career and compensation, see our salary negotiation guide.
Data Scientist Salary by Location
Geographic location significantly impacts data scientist pay. Major tech and financial hubs offer the highest nominal salaries, though cost of living—especially housing—often consumes much of the difference.
Major Tech and Finance Hubs
California (Bay Area, Los Angeles), New York, Washington (Seattle), and Massachusetts (Boston) consistently show the highest data scientist salaries. Mid-level data scientists in San Francisco or New York routinely earn $140,000–$180,000+ in base salary alone, with total compensation potentially much higher at top companies.
However, housing costs in these markets are extreme. A $180,000 salary in San Francisco may not go as far as $130,000 in Austin or Denver when rent differences are factored in.
Growing Markets
Texas (Austin, Dallas), Colorado (Denver), North Carolina (Research Triangle), Georgia (Atlanta), and Washington D.C. offer competitive salaries with lower costs of living than traditional tech hubs. These markets have seen significant growth in data science hiring as companies expand remote workforces and establish satellite offices.
Remote Work Impact
Remote work has complicated location-based pay for data scientists. Some companies pay the same regardless of location; others adjust compensation based on cost-of-living zones. A data scientist working remotely for a Bay Area company while living in a lower-cost city may see significant purchasing power advantages—or may receive location-adjusted pay.
| Region / Market Type | Typical Effect on Pay | Notes |
|---|---|---|
| Major tech & finance hubs (CA, NY, WA, MA) | Higher nominal salaries | Strong demand but higher living costs |
| Growing markets (TX, CO, NC, GA, D.C.) | Competitive pay, better cost-adjusted value | Mix of tech, healthcare, and corporate roles |
| Smaller markets / remote roles | More varied; sometimes lower nominal | Some remote jobs still pay near big-market rates |
For context on how these figures compare to typical US earnings, see our guide to average salary in the US.
Data Scientist Salary by Industry
Industry choice has a significant impact on data scientist compensation. Where you apply your skills matters almost as much as the skills themselves.
Technology and AI Companies
Tech companies—especially FAANG, high-growth startups, and AI-focused firms—typically pay at the high end for data scientists. These companies rely heavily on data-driven decision making and often compete aggressively for talent. Expect the highest base salaries plus meaningful stock and bonus components.
Finance and Insurance
Investment banks, hedge funds, fintech companies, and insurance firms pay premium compensation for data scientists with quantitative skills. Roles involving risk modeling, algorithmic trading support, or fraud detection often command salaries comparable to or exceeding tech industry rates. The tradeoff may be longer hours or less flexibility.
Healthcare and Biotech
Healthcare organizations, pharmaceutical companies, and biotech firms increasingly hire data scientists for clinical research, drug discovery, population health analysis, and operations optimization. Pay is generally competitive, though sometimes slightly below pure tech, with potential mission-driven appeal.
Retail, E-commerce, and Consumer
Large retailers and e-commerce companies (Amazon, Walmart, Target, etc.) hire extensively for recommendation systems, pricing optimization, supply chain analytics, and customer behavior modeling. Pay is often competitive with tech, especially at companies where data science directly drives revenue.
Public Sector and Non-Profits
Government agencies, research institutions, and non-profits typically pay below market rates but offer other benefits: job stability, pension systems, mission alignment, and sometimes better work-life balance. A data scientist at a federal agency might earn 20–40% less than at a tech company but with different lifestyle tradeoffs.
For more on how different tech roles compare, see our industry comparison guide.
How Skills and Education Affect Pay
Technical skills and educational background strongly influence data scientist salary. Both what you know and how you can prove it matter.
Education and Degrees
Many data science roles prefer or require advanced degrees. Multiple salary surveys show that data scientists with Master's degrees earn more on average than those with Bachelor's degrees, and PhDs command additional premiums in research-oriented roles. However, experience and demonstrated skills can offset degree differences—a bootcamp graduate with five years of strong project work may earn more than a fresh PhD.
High-Value Technical Skills
Certain skills consistently correlate with higher compensation: machine learning and deep learning frameworks (TensorFlow, PyTorch), natural language processing, computer vision, MLOps and model deployment, cloud platforms (AWS, GCP, Azure), and big data tools (Spark, distributed computing). Data scientists who can not only build models but also deploy and maintain them in production often earn more.
Domain Expertise
Deep knowledge in specific domains—healthcare regulations, financial markets, supply chain logistics, advertising technology—adds value beyond pure technical skills. A data scientist who understands both the modeling techniques and the business context can drive more impact and often commands higher pay.
Related Roles
Adjacent roles like machine learning engineer and software engineer have overlapping skills with different compensation profiles. ML engineers often earn slightly more than data scientists due to stronger engineering requirements; software engineers have broader demand but similar ceiling potential at top companies.
How to Increase Your Data Scientist Salary
Compensation growth in data science comes from strategic moves, not just tenure.
Build High-Impact Projects
Focus on projects with measurable business impact: models that increase revenue, reduce costs, or improve user experience in quantifiable ways. Document outcomes in terms business leaders understand. A portfolio of high-impact work is the foundation for negotiating higher pay.
Develop In-Demand Skills
Stay current with skills the market values. In 2025, that includes deep learning, LLMs and generative AI, MLOps, and real-time systems. Data scientists who can deploy models to production (not just prototype in notebooks) are particularly valuable.
Target Higher-Paying Industries
Moving from a lower-paying industry to tech, finance, or high-value e-commerce can yield 20–40% salary increases for equivalent roles. The same skills applied in different contexts have very different market values.
Negotiate Effectively
Many data scientists leave money on the table by not negotiating offers. Research market rates for your role and experience level, collect competing offers when possible, and negotiate—companies expect it. Our salary negotiation guide covers specific tactics.
Change Companies Strategically
Data scientists who switch employers every 2–4 years typically see faster compensation growth than those who stay in place. Each move is an opportunity to reset your salary at current market rates, especially if you've developed new skills since your last negotiation.
Career Path and Related Roles
Typical Career Progression
The standard individual contributor track progresses from Data Scientist → Senior Data Scientist → Staff/Principal Data Scientist → Distinguished Data Scientist (at some companies). Each level brings higher compensation and broader scope—from executing on defined projects to setting technical direction for teams or organizations.
Management paths diverge into Data Science Manager → Director of Data Science → VP/Head of Data Science. Management compensation often matches or exceeds IC compensation at equivalent levels.
Common Entry Points
Many data scientists start as data analysts, business intelligence analysts, or in adjacent quantitative roles before transitioning. Others enter directly from graduate programs in statistics, computer science, physics, or economics. The diversity of backgrounds means multiple paths into the field.
Related Technical Roles
Data science skills transfer to several adjacent roles with their own compensation profiles:
Machine Learning Engineers focus on building and deploying ML systems at scale—typically higher engineering requirements and often slightly higher pay. Software Engineers with ML specializations bridge data science and engineering. Data Engineers build the infrastructure data scientists depend on. Analytics Engineers combine data engineering with business-facing analytics.
Your choice should balance earning potential with what you actually enjoy doing day-to-day.
Frequently Asked Questions
What is the average data scientist salary in the US in 2025?
Median total compensation for data scientists in the US is approximately $120,000–$140,000 for mid-level roles, with significant variation by experience, location, and industry. Entry-level positions start around $90,000–$115,000, while senior and staff data scientists earn $160,000–$300,000+.
How much does an entry-level data scientist make?
Entry-level data scientists with 0–2 years of experience typically earn $90,000–$115,000 in total compensation. Factors that push toward the higher end include advanced degrees, strong programming skills, relevant internships, and landing a role at a well-funded company or in a major market.
Which industries pay data scientists the most?
Technology companies (especially FAANG and AI-focused firms), finance (investment banks, hedge funds, fintech), and high-value e-commerce/retail typically pay the highest for data scientists. Healthcare and biotech also offer competitive pay. Public sector and non-profits generally pay below market but offer different benefits.
Which locations are best for high data scientist pay?
California (Bay Area), New York, Washington (Seattle), and Massachusetts (Boston) offer the highest nominal salaries. However, high cost of living offsets much of the difference. Growing markets like Texas, Colorado, and North Carolina offer competitive salaries with better purchasing power.
Is data science still a good career choice in 2025?
Yes. Data science remains one of the fastest-growing, highest-paying career paths. The Bureau of Labor Statistics projects continued strong growth, and demand spans virtually every industry. However, the field has matured—entry barriers are higher than a few years ago, and employers increasingly expect both statistical sophistication and engineering capabilities. Continuous learning and demonstrated impact matter more than ever.