Data Scientist Salary Guide 2025 — Pay by Experience & Location

The median annual wage for data scientists reached $112,590 in May 2024, with the occupation projected to grow 34 percent through 2034 — making it one of the fastest-growing professions in the country [1][2].

Key Takeaways

• Data scientists earned a median of $112,590 per year as of May 2024, with the 90th percentile exceeding $194,410 [1].
• Washington state leads all states at $158,760 in mean annual wages, followed by Washington, D.C. ($137,120) and California ($136,800) [3].
• The San Jose metro area reports the highest metropolitan wages at $173,160 median, followed by San Francisco-Oakland at $166,300 [3].
• Employment of data scientists is projected to grow 34 percent from 2024 to 2034, adding approximately 83,600 new positions over the decade [2].
• Specialization in machine learning engineering and deep learning can push total compensation well above $200,000 at mid-career [4].

National Salary Overview

According to the Bureau of Labor Statistics Occupational Employment and Wage Statistics program, data scientists (SOC 15-2051) earned a median annual wage of $112,590 and a median hourly wage of $54.13 as of May 2024 [1]. Approximately 245,900 data scientists were employed across the United States, a figure that has grown rapidly since the BLS began tracking this occupation as a distinct category [2].

The percentile distribution reveals a wide earning band. At the 10th percentile, data scientists earned $63,650 annually, reflecting entry-level positions at smaller organizations or in lower-cost markets [1]. The 25th percentile stood at $82,630, the 75th percentile reached $155,810, and the top 10 percent of earners made at least $194,410 per year [1]. The $130,760 spread between the 10th and 90th percentiles underscores how dramatically compensation scales with expertise, industry, and employer type.

Compared to the national median annual wage for all occupations of $49,500, data scientists at the median earn approximately 2.3 times the benchmark [5]. Even entry-level data scientists at the 10th percentile out-earn the national median for all workers by $14,150.

The 34 percent projected growth rate from 2024 to 2034 ranks data science among the top ten fastest-growing occupations in the BLS Occupational Outlook Handbook [2]. This growth is driven by the expanding volume of data generated across industries, the maturation of AI and machine learning applications, and the increasing recognition that data-driven decision-making provides measurable competitive advantages. About 17,700 openings for data scientists are projected each year over the decade [2].

Salary by Experience Level

Experience and the ability to translate data into business outcomes are the primary drivers of data scientist compensation. While the BLS does not segment by experience directly, industry surveys and job-posting analyses provide clear benchmarks.

Entry-Level (0-2 years): Data scientists entering the field with a master's degree or equivalent bootcamp training typically earn $65,000 to $90,000 in base salary [4]. These roles focus on exploratory analysis, building dashboards, and supporting senior team members on modeling projects. At technology companies, total compensation (including signing bonuses) can reach $100,000-$130,000.

Mid-Career (3-7 years): Data scientists who can independently scope and execute end-to-end projects earn $95,000 to $140,000 in base salary [4]. Those who combine statistical rigor with engineering skills (deploying models to production, building data pipelines) command premiums at the higher end. Total compensation at competitive employers reaches $150,000-$200,000.

Senior (8-15 years): Senior and staff data scientists who lead teams, define modeling strategies, and influence product direction earn $140,000 to $190,000 in base salary [1][4]. At major technology companies, total compensation packages (base, bonus, equity) range from $220,000 to $350,000. The transition from individual contributor to technical leader is the key inflection point.

Principal/Director (15+ years): Directors of data science and principal scientists earn $180,000 to $250,000+ in base salary, with total compensation at top-tier companies reaching $350,000 to $500,000+ [4]. These roles require a combination of deep technical expertise, business acumen, and the ability to manage cross-functional teams.

Top-Paying States

State-level compensation for data scientists varies dramatically based on the density of technology employers, research institutions, and federal agencies. The following states offered the highest mean annual wages [3]:

Washington's commanding lead ($158,760) reflects the concentration of Amazon, Microsoft, and a growing cluster of AI-focused startups in the Seattle-Bellevue corridor [3]. When adjusted for cost of living using Bureau of Economic Analysis Regional Price Parities, Washington still leads with an effective salary of approximately $146,239, making it the best-value state for data scientists [3].

California's $136,800 mean is driven by the San Francisco Bay Area and Silicon Valley, where data science roles at technology companies routinely exceed $150,000 [3]. Virginia and Maryland's strong showings reflect the density of defense contractors, intelligence agencies, and federal data initiatives in the Washington, D.C. metropolitan area.

Top-Paying Metro Areas

Metropolitan-level data reveals the sharpest pay premiums [3]:

The $60,570 gap between San Jose ($173,160) and the national median ($112,590) reflects the hyper-competitive market for data talent in Silicon Valley, where companies like Google, Meta, Apple, and Netflix maintain large data science organizations [3]. San Francisco follows closely, buoyed by a concentration of AI/ML startups and established technology firms.

Remote work has expanded geographic flexibility for data scientists, though many employers have moved to hybrid models requiring partial in-office presence. Companies maintaining location-agnostic pay bands effectively provide data scientists in lower-cost areas with significant purchasing-power advantages.

Salary by Specialization

The broad umbrella of "data scientist" encompasses several distinct specializations, each with different compensation profiles [4]:

Machine Learning Engineering: Data scientists who can build and deploy production ML systems command the highest premiums, often earning 20-30 percent above general data science roles. The scarcity of professionals who combine statistical expertise with software engineering skills drives this premium.

AI Research: Research scientists at organizations like Google DeepMind, Meta FAIR, and OpenAI earn among the highest salaries in the field, with total compensation packages exceeding $300,000 at the senior level. These roles typically require a PhD and published research.

Analytics and Business Intelligence: Data scientists focused on descriptive analytics, A/B testing, and business reporting tend to earn closer to the 25th-50th percentile range ($82,630-$112,590) [1]. While critically important, these roles face more competition from business analysts and BI specialists.

Natural Language Processing and Computer Vision: Specialists in NLP and CV earn 15-25 percent premiums, driven by the explosion of generative AI applications and the technical depth required to work with transformer architectures and diffusion models.

Quantitative Finance: Data scientists in hedge funds and proprietary trading firms can earn total compensation exceeding $400,000, reflecting the direct revenue impact of their modeling work.

Benefits and Total Compensation

Data scientists at technology companies typically receive comprehensive benefits packages that significantly augment base salary. Standard components include equity compensation (RSUs or stock options) that often add 20-60 percent to base salary at public companies, annual performance bonuses of 10-20 percent, and signing bonuses ranging from $10,000 to $50,000 depending on level and competition [4].

Health insurance (medical, dental, vision), 401(k) matching (typically 50 percent match up to 4-6 percent of salary), and 15-20 days of paid time off are standard across employers. Many technology companies add professional development budgets ($2,000-$5,000 annually for conferences and courses), computing hardware allowances, and home office stipends for remote workers.

Data scientists in healthcare, financial services, and consulting may receive different compensation structures, with higher base salaries but smaller or no equity components. Consulting firms often provide per-diem travel allowances and premium healthcare coverage.

The total compensation premium over base salary ranges from 15-25 percent at traditional employers to 50-100 percent at major technology companies, making it essential to evaluate offers holistically rather than comparing base salaries alone.

How to Negotiate Salary

Data science is a high-demand field where informed negotiation can yield substantial returns. These strategies are specific to the profession:

1. Lead with impact metrics. Quantify the business value of your previous work: revenue generated by recommendation systems, cost savings from predictive maintenance models, or efficiency gains from automation. Data science is uniquely positioned to demonstrate ROI.

2. Understand the company's ML maturity. Companies building their first data team often pay premiums for experienced hires who can establish infrastructure and best practices. Companies with mature data organizations offer more structured compensation but clearer growth paths.

3. Benchmark against level, not just title. "Data Scientist" at Google (L4) and "Data Scientist" at a Series A startup describe fundamentally different scope. Use Levels.fyi and Glassdoor to compare within level bands [4].

4. Negotiate equity separately from base. At public companies, RSU grants are often more flexible than base salary bands. An additional $20,000 in annual equity vesting can be easier to secure than a $20,000 base increase.

5. Highlight rare technical skills. Proficiency in production ML systems (MLOps, Kubernetes, Spark), deep learning frameworks, and causal inference methods commands measurable premiums. Name these skills explicitly during negotiation.

6. Use the growth projection as leverage. With 34 percent projected job growth, employers know that data scientists have options [2]. You are negotiating in a seller's market.

7. Consider the full picture. Remote flexibility, conference budgets, GPU computing credits, and publication support may have significant value that does not appear in a salary comparison.

Salary Growth and Career Progression

A data scientist's compensation trajectory can follow several distinct paths. The individual contributor (IC) track moves from data scientist to senior data scientist to staff/principal data scientist, with total compensation growing from approximately $100,000 to $300,000+ over 10-15 years at competitive employers [4].

The management track transitions to data science manager, director, and VP of data science. Directors at mid-size technology companies earn $200,000-$300,000 in total compensation, while VPs at large enterprises can exceed $400,000 [4].

Key inflection points include the first promotion to senior (typically a 20-30 percent total compensation increase), the transition to staff level or management (a 30-50 percent increase), and taking on organization-wide scope as a director (another 30-40 percent increase).

The fastest salary growth occurs in years 3-8, when data scientists move from executing assigned analyses to leading projects and building systems. Engineers who develop "full-stack" data science skills — from data engineering to modeling to deployment — advance most rapidly because they can deliver end-to-end impact without depending on adjacent teams.

Key Takeaways and Next Steps

Data science offers exceptional compensation with a median of $112,590 and growth to $194,410+ at the 90th percentile [1]. The 34 percent projected job growth through 2034 ensures sustained demand [2]. Specialization in ML engineering, geographic targeting toward high-paying markets, and effective negotiation can each add $20,000-$50,000 to annual compensation.

To compete for the highest-paying data science roles, your resume must demonstrate both technical depth and business impact. Try ResumeGeni's AI-powered resume builder to optimize your data science resume for ATS systems and highlight the quantitative achievements that hiring managers value most.

Frequently Asked Questions

What is the starting salary for a data scientist? Entry-level data scientists with 0-2 years of experience typically earn between $65,000 and $90,000 in base salary [4]. The BLS 10th percentile ($63,650) represents the lowest-paid segment of the profession [1].

Which state pays data scientists the most? Washington leads at $158,760 in mean annual wages, followed by Washington, D.C. ($137,120) and California ($136,800) [3]. Washington also ranks first when salaries are adjusted for cost of living.

How much does a senior data scientist make? Senior data scientists (8-15 years of experience) earn $140,000 to $190,000 in base salary [4]. Total compensation at major technology companies ranges from $220,000 to $350,000 including equity and bonuses.

Is data science still a good career in 2025? Yes. With 34 percent projected growth through 2034 and a median salary more than double the national all-occupation median, data science remains among the most promising career paths in the American economy [1][2][5].

What is the difference between a data scientist and a data analyst salary? Data scientists earn a median of $112,590 compared to approximately $83,000-$95,000 for data analysts [1]. The gap reflects the additional statistical modeling, programming, and machine learning skills required of data scientists.

How much do data scientists at FAANG companies make? Total compensation at major technology companies ranges from $130,000 for entry-level to $350,000+ for senior data scientists, with staff-level roles exceeding $400,000 when including equity grants [4].

Do data scientists need a PhD? A PhD is not required for most data science positions, though it commands a salary premium of approximately 10-15 percent and is often preferred for research-focused roles at AI labs. A master's degree with relevant experience is sufficient for the majority of industry positions.

Salary data sourced from the Bureau of Labor Statistics Occupational Employment and Wage Statistics program, May 2024 survey. Figures represent base wages and do not include benefits, bonuses, or equity compensation unless otherwise noted.