Data science Salary

Data science salary” is a common but complex topic because it varies dramatically based on location, experience, industry, and specific sk ill s.

Here’s a comprehensive breakdown of data science salaries, focusing primarily on the United States market, with insights into global trends.

Table of Contents

Executive Summary: Key Takeaways

High Earning Potential: Data science remains one of the highest-paying professions.
Experience is King: Salary increases significantly with years of experience.
Location Matters: Tech hubs (SF, NYC, Seattle) pay much more than other areas.
Specialization Pays: Roles in ML, AI, and specialized domains (e.g., NLP, Computer Vision) command premium salaries.
Industry Impact: Tech, Finance, and Healthcare are the top-paying sectors.

1. Salary by Experience Level (USA)

This is the single biggest factor. Data is from sources like Glassdoor, Levels.fyi, and LinkedIn Salary for 2026.

Experience Level	Typical Title(s)	Average Base Salary Range (USD)	Total Compensation (with bonus/stock)
Entry-Level	Data Scientist I, Junior Data Scientist	$85,000 – $120,000	$90,000 – $130,000
Mid-Level	Data Scientist II, Senior Data Scientist	$120,000 – $160,000	$130,000 – $200,000+
Senior-Level	Senior Data Scientist, Staff Data Scientist, Principal DS	$150,000 – $220,000	$180,000 – $300,000+
Leadership	Lead Data Scientist, Manager, Director, VP	$180,000 – $250,000+	$220,000 – $500,000+

Note: “Total Compensation” at major tech companies (FAANG) can be 1.5x to 2x the base salary due to annual bonuses and stock grants (RSUs).

2. Salary by Location (USA)

Cost of living and concentration of tech companies create huge disparities.

Location Tier	Example Cities	Salary Adjustment (vs. National Average)
Tier 1: High Cost	San Francisco Bay Area, New York City, Seattle	+15% to +25%
Tier 2: Major Tech Hubs	Boston, Austin, Los Angeles, San Diego, Washington D.C.	+/- 5% (Near National Average)
Tier 3: Growing Hubs	Atlanta, Chicago, Denver, Raleigh-Durham	-5% to -10%
Tier 4: Other Metros	Phoenix, Dallas, Philadelphia, Columbus	-10% to -15%

Example: A Senior Data Scientist making $150,000 in Austin might command $180,000+ in San Francisco, but the purchasing power could be similar or even lower in SF.

3. Salary by Role and Specialization

Not all “Data Scientists” do the same thing. Specialized skills are highly rewarded.

Role / Specialization	Key Skills	Average Salary Range (USD – Mid-Level)	Why it Pays More
Machine Learning Engineer	ML Sys Design, Deep Learning, MLOps, Python, TensorFlow/PyTorch	$140,000 – $190,000	High demand for building and deploying scalable ML models.
Data Engineer	Spark, Kafka, AWS/Azure/GCP, Data Pipelines, SQL, Scala	$130,000 – $180,000	Foundational role; without clean data, nothing else works.
AI / NLP Scientist	Transformers, LLMs, LangChain, RAG, PyTorch	$150,000 – $220,000+	Cutting-edge field with massive demand post-ChatGPT.
Business Intelligence / Analytics	SQL, Tableau/Power BI, A/B Testing, Statistics	$100,000 – $140,000	More focused on insights and reporting than building complex models.
Generalist Data Scientist	Python, SQL, Stats, ML, Visualization	$120,000 – $160,000	The classic “unicorn” role, very common.

4. Salary by Industry

The industry you work in significantly impacts your pay.

Industry	Average Salary (Senior Level)	Notes
Technology / FAANG	$180,000 – $300,000+	Highest pay, heavy on stock compensation.
Finance & FinTech	$160,000 – $250,000+	High stakes, high rewards. Bonuses are significant.
Healthcare / Pharma	$150,000 – $220,000	Specialized domain knowledge is highly valued.
Consulting	$140,000 – $200,000	Travel-heavy, but great for career growth.
Retail / E-commerce	$130,000 – $180,000	Focus on recommendation systems, logistics, pricing.

5. Global Data Science Salaries

Salaries are highest in the US, but other markets are growing rapidly. Figures are converted to USD for comparison and represent annual gross salaries.

Canada: C$80,000 – C$150,000 (approx. $60,000 – $110,000 USD). Toronto and Vancouver are the main hubs.
United Kingdom: £50,000 – £90,000 (approx. $63,000 – $115,000 USD). London dominates the market.
European Union:
- Germany: €60,000 – €85,000 (approx. $65,000 – $92,000 USD). Berlin and Munich are key cities.
- Netherlands: €55,000 – €80,000 (approx. $60,000 – $87,000 USD).
- Note: European salaries are generally lower than in the US but come with stronger social benefits (e.g., more vacation, healthcare).
India: ₹800,000 – ₹2,500,000+ (approx. $10,000 – $30,000+ USD). The range is vast, with top-tier companies paying very competitively within the local economy. Bangalore is the primary hub.
Australia: A$110,000 – A$180,000 (approx. $72,000 – $118,000 USD). Sydney and Melbourne are the main markets.

How to Increase Your Data Science Salary

Master In-Demand Skills: Focus on Machine Learning Engineering (MLOps, cloud deployment), LLMs/Generative AI, and Deep Learning. Proficiency in Python, SQL, and Spark is non-negotiable.
Gain Domain Expertise: Become an expert in a specific field like finance, bioinformatics, or marketing. Domain knowledge makes you indispensable.
Build a Strong Portfolio: Showcase your skills with real-world projects on GitHub. A portfolio is more powerful than just a resume.
Get Certified (Strategically): While not a substitute for experience, certifications in cloud platforms (AWS, Azure, GCP) or specific tools can validate your sk i lls.
Practice Communication & Storytelling: The ability to explain complex results to non-technical stakeholders is a rare and highly valued skill.
Negotiate Your Offer: Always negotiate! Research the market rate for your role, location, and experience level. Remember to negotiate total compensation (base, bonus, stock, benefits), not just the base salary.

Reliable Salary Data Sources

Levels.fyi: The best for tech company compensation, especially total comp breakdowns.
Glassdoor: Good for base salary estimates and company reviews.
LinkedIn Salary: Uses its vast user data to provide insights.
H1B Salary Database: Provides actual salary data for jobs sponsored for work visas in the US (a very accurate source for US tech salaries).

The field is dynamic, and salaries for roles in AI and LLMs are currently seeing especially rapid growth. Staying skilled-up is the best strategy for maximizing your earning potential.