Entry Level Data Science Jobs Salary In USA: Your Complete Guide to Starting Your Data Career

Are you considering a career in data science but wondering what kind of salary you can expect as a newcomer to the field? You're not alone.

As data continues to drive business decisions across every industry, the demand for skilled data scientists has skyrocketed, making it one of the most sought-after career paths in the technology sector.

But here's the question that keeps many aspiring data scientists up at night:

What can you realistically expect to earn in your first data science role?

The answer isn't as straightforward as you might think, but it's definitely encouraging.

In this comprehensive guide, we'll break down everything you need to know about entry-level data science jobs' salaries in the USA, including current market rates, factors that influence your earning potential, and actionable strategies to maximize your starting salary.

Whether you're a recent graduate, career changer, or someone looking to understand the financial landscape of data science, this guide will give you the insider knowledge you need to make informed decisions about your career.

Current Entry-Level Data Science Salary

The data science job market has undergone significant evolution over the past few years, and 2025 presents both exciting opportunities and realistic expectations for new entrants. Let's dive into the numbers that matter most to your career planning.

The Big Picture: What Entry-Level Data Scientists Actually Earn

The average salary for an entry-level data Scientist is $111,496 per year in the United States, according to recent Glassdoor data. However, this figure tells only part of the story.

The reality is that entry-level data science salaries vary dramatically based on several key factors.

Here's what you can expect across different salary reporting platforms:

Salary Range Overview:

Glassdoor: $111,496 average
Interview Query: $60K to $130K range
Indeed: $65,052 average
PayScale: $88,108 for less than 1 year of experience
365 Data Science: $117,276 average entry-level

The wide range in these figures reflects the diverse nature of data science roles and the various factors that influence compensation. Let's explore why these numbers vary so significantly.

Understanding the Salary Spectrum

The entry-level data science salary landscape isn't uniform. Here's how the market typically breaks down:

Lower End ($60K - $80K):

Smaller companies or startups
Non-tech industries
Lower cost-of-living areas
Roles that are more analyst-focused

Mid-Range ($80K - $120K):

Mid-sized companies
Mixed tech and traditional industries
Moderate cost-of-living areas
True data scientist roles with modeling components

Upper End ($120K - $165K):

Large tech companies
High-growth startups with funding
High cost-of-living areas (SF Bay Area, NYC, Seattle)
Roles requiring specialized skills or advanced degrees

The Complete Compensation Picture

When evaluating entry-level data science opportunities, it's crucial to look beyond base salary. Many top-tier companies also reward data scientist engineers with annual variable cash compensation in the range of $18,965 to $35,401 for entry-level positions.

Additional Compensation Elements:

Bonuses: 10-25% of base salary
Stock Options: Particularly valuable at tech companies
Health Benefits: Comprehensive medical, dental, and vision
Retirement Plans: 401(k) matching
Professional Development: Training budget, conference attendance
Flexible Work: Remote work options, flexible schedules

Key Factors That Influence Your Entry-Level Data Science Salary

Understanding what drives salary differences is crucial for positioning yourself effectively in the job market. Let's examine the primary factors that can significantly impact your earning potential.

Geographic Location: Where You Work Matters Most

Location remains one of the most significant determinants of your entry-level data science salary. The cost of living and local demand for data talent create substantial regional variations.

Top-Paying Cities for Entry-Level Data Scientists:

San Francisco Bay Area:

Average entry-level salary: $130K - $165K
Cost of living adjustment: Very high
Tech company concentration: Highest

New York City:

Average entry-level salary: $115K - $145K
Financial services demand: High
Living costs: Very high

Seattle:

Average entry-level salary: $110K - $140K
Tech giants' presence: Amazon, Microsoft
Living costs: High

Boston:

Average entry-level salary: $100K - $130K
Biotech and healthcare focus: Strong
Living costs: High

Austin:

Average entry-level salary: $95K - $125K
Emerging tech hub: Growing
Living costs: Moderate to high

Lower Cost, Competitive Salary Cities:

Denver: $85K - $115K
Atlanta: $80K - $110K
Chicago: $85K - $115K
Phoenix: $75K - $105K

Industry Impact on Data Science Salaries

The industry you choose to work in significantly affects your compensation package. Some sectors consistently pay premium salaries for data science talent.

Highest-Paying Industries:

Technology Companies:

Salary range: $120K - $165K
Why they pay more: Data is core to their business model
Examples: Google, Facebook, Amazon, Netflix

Financial Services:

Salary range: $110K - $150K
Why they pay more: High-value decision making, regulatory requirements
Examples: JPMorgan Chase, Goldman Sachs, hedge funds

Healthcare & Biotech:

Salary range: $100K - $140K
Why they pay more: Complex data, life-saving applications
Examples: Pfizer, Johnson & Johnson, biotech startups

Consulting:

Salary range: $95K - $130K
Why they pay more: Client billing rates, expertise premium
Examples: McKinsey, Deloitte, Accenture

Traditional Industries (Growing Opportunity):

Retail: $75K - $105K
Manufacturing: $80K - $110K
Energy: $85K - $115K
Government: $70K - $100K

Education and Skill Requirements

Current research shows that 76.6% of data scientists hold a master's or PhD degree, and 19.8% have a bachelor's degree. While education matters, practical skills often carry equal or greater weight in salary negotiations.

Education Level Impact:

Bachelor's Degree:

Average salary range: $88,151 - $93,553 per year
Requirements: Strong portfolio, relevant projects
Path: Often requires additional self-learning

Master's Degree:

Average salary range: $85,000 - $105,000 per year
Advantage: Structured learning, networking opportunities
Common programs: Data Science, Statistics, Computer Science

PhD:

Salary premium: $10K - $20K above master's level
Advantage: Research experience, specialized knowledge
Trade-off: Longer time to market

Alternative Paths:

Bootcamps: 3-6 month intensive programs
Online Certifications: Coursera, edX, Udacity
Self-taught: Portfolio-driven approach

Technical Skills That Command Higher Salaries

Certain technical skills can significantly boost your entry-level salary prospects. Here's what employers are willing to pay a premium for:

Programming Languages (Salary Impact):

Python: Base expectation, $0 premium
R: Statistical focus, $2K - $5K premium
SQL: Essential skill, $0 premium
Java/Scala: Big data environments, $5K - $10K premium
JavaScript: Full-stack capabilities, $3K - $8K premium

Machine Learning Frameworks:

TensorFlow/PyTorch: Deep learning, $5K - $15K premium
Scikit-learn: Standard ML, base expectation
Keras: Neural networks, $3K - $8K premium

Cloud Platforms:

AWS: Most in-demand, $5K - $12K premium
Google Cloud Platform: Growing demand, $3K - $8K premium
Microsoft Azure: Enterprise focus, $4K - $10K premium

Big Data Tools:

Spark: Distributed computing, $8K - $15K premium
Hadoop: Legacy but valuable, $5K - $10K premium
Kafka: Real-time data, $5K - $12K premium

Maximizing Your Entry-Level Data Science Salary

Now that you understand the landscape, let's focus on actionable strategies to maximize your earning potential from day one.

Building a Competitive Skill Portfolio

Your technical skills are your currency in salary negotiations. Here's how to build a portfolio that commands top dollar:

Essential Technical Foundation:

Master Python and SQL: These are non-negotiable basics
Learn Statistical Analysis: Understanding of statistics and probability
Gain Machine Learning Experience: Supervised and unsupervised learning
Develop Data Visualization Skills: Tableau, Power BI, or Python libraries
Practice Data Cleaning: Real-world data is messy

Advanced Skills for Salary Boost:

Deep Learning: Neural networks, computer vision, NLP
Cloud Computing: AWS, GCP, or Azure certifications
MLOps: Model deployment and monitoring
A/B Testing: Experimental design and analysis
Business Intelligence: Understanding of business metrics

Creating an Impressive Portfolio

Your portfolio is often more important than your resume in landing a high-paying entry-level position. Here's how to build one that stands out:

Portfolio Project Ideas:

Predictive Analytics Project: Predict customer churn, sales, or market trends
NLP Project: Sentiment analysis, text classification, or chatbot
Computer Vision Project: Image recognition, object detection
Time Series Analysis: Financial forecasting, demand prediction
Recommendation System: Similar to Netflix or Amazon recommendations

Portfolio Best Practices:

GitHub Repository: Clean, well-documented code
Jupyter Notebooks: Step-by-step analysis with explanations
Blog Posts: Write about your projects and learnings
Live Demos: Deploy models using Streamlit or Flask
Business Impact: Explain the real-world value of your work

Networking and Professional Development

Building relationships in the data science community can significantly impact your salary prospects:

Networking Strategies:

Join Data Science Communities: Kaggle, Reddit, Stack Overflow
Attend Meetups and Conferences: Local data science groups, PyData, Strata
Connect with Professionals: LinkedIn outreach, informational interviews
Contribute to Open Source: GitHub contributions, package development
Build a Personal Brand: Twitter, LinkedIn, Medium articles

Negotiation Tactics for Entry-Level Positions

Even as an entry-level candidate, you have negotiation power. Here's how to use it effectively:

Salary Negotiation Framework:

Research Market Rates: Use multiple sources (Glassdoor, PayScale, levels.fyi)
Highlight Unique Value: Emphasize your specific skills and projects
Consider Total Compensation: Base salary, bonus, benefits, growth potential
Ask for Time: Don't accept the first offer immediately
Practice Your Pitch: Prepare specific examples of your value

Common Negotiation Points:

Base Salary: 5-15% increase often possible
Signing Bonus: Compensate for skills or relocation
Professional Development: Training budget, conference attendance
Flexible Work Arrangements: Remote work, flexible hours
Accelerated Review Timeline: Faster path to promotion

Career Progression and Long-Term Earning Potential

Understanding the career trajectory in data science helps you make informed decisions about your first role and long-term earning potential.

Typical Career Path and Salary Growth

Experience increases salary, with entry-level data scientists earning around $117,276 and experienced professionals nearing $190,000.

Career Progression Timeline:

Years 0-2: Junior Data Scientist

Salary range: $65K - $130K
Focus: Learning foundations, building skills
Key activities: Data cleaning, basic modeling, visualization

Years 2-5: Data Scientist

Salary range: $90K - $160K
Focus: Independent project ownership
Key activities: Advanced modeling, A/B testing, stakeholder communication

Years 5-8: Senior Data Scientist

Salary range: $130K - $200K
Focus: Technical leadership, mentoring
Key activities: Architecture decisions, complex projects, team leadership

Years 8+: Principal Data Scientist / Data Science Manager

Salary range: $180K - $300K+
Focus: Strategic direction, team management
Key activities: Product strategy, team building, organizational impact

Specialization Opportunities

As you progress, specializing in high-demand areas can significantly boost your earning potential:

High-Value Specializations:

Machine Learning Engineering: $150K - $250K
Deep Learning/AI: $140K - $230K
Data Engineering: $130K - $220K
Product Analytics: $120K - $200K
Quantitative Finance: $150K - $300K+

Common Mistakes to Avoid When Evaluating Salary Offers

Learning from others' mistakes can save you thousands of dollars and career setbacks. Here are the most common pitfalls to avoid:

Focusing Only on Base Salary

Many entry-level candidates make the mistake of comparing offers based solely on base salary. This approach can cost you significantly in the long run.

What to Consider Beyond Base Salary:

Equity/Stock Options: Can be worth more than base salary at successful companies
Bonus Structure: Annual bonuses, performance incentives
Benefits Value: Health insurance, retirement matching
Professional Development: Training budget, conference attendance
Work-Life Balance: Vacation time, flexible work arrangements

Accepting the First Offer

Research shows that most employers expect some level of negotiation. Accepting the first offer without discussion often leaves money on the table.

Better Approach:

Thank them for the offer
Ask for time to consider (24-48 hours)
Research comparable salaries
Prepare your negotiation points
Counter with specific, reasonable requests

Ignoring Company Culture and Growth Potential

A higher salary at a company with poor culture or limited growth opportunities can be a career mistake.

Evaluation Criteria:

Learning Opportunities: Mentorship, challenging projects
Career Advancement: Promotion timeline, skill development
Team Dynamics: Collaboration, support system
Company Trajectory: Growth potential, market position
Technology Stack: Modern tools, innovation opportunities

Not Considering Cost of Living

A $120K salary in San Francisco has very different purchasing power than the same salary in Austin or Denver.

Cost of Living Factors:

Housing: Rent/mortgage, property taxes
Transportation: Car payments, gas, public transit
Taxes: State income tax, local taxes
General Expenses: Food, utilities, entertainment
Quality of Life: Commute time, weather, amenities

Industry Trends Affecting Entry-Level Salaries

Staying informed about industry trends helps you position yourself for the best opportunities and highest salaries.

Remote Work Impact

The COVID-19 pandemic permanently changed the data science job market. The average salary for a data scientist who works from home is $122,738 per year, though remote positions are typically competitive and may generally go to those with the most experience.

Remote Work Considerations:

Salary Arbitrage: Live in lower-cost areas while earning high salaries
Increased Competition: Access to a global talent pool
Skill Premium: Remote positions often require higher skill levels
Communication Skills: Written and verbal communication is more important

AI and Machine Learning Specialization

The rise of AI and machine learning has created premium opportunities for specialized skills:

High-Demand AI Skills:

Natural Language Processing: $10K - $20K salary premium
Computer Vision: $8K - $18K salary premium
Reinforcement Learning: $15K - $25K salary premium
MLOps: $12K - $22K salary premium

Industry-Specific Opportunities

Certain industries are experiencing explosive growth in data science demand:

Healthcare Data Science:

Salary growth: 15-20% annually
Drivers: Personalized medicine, drug discovery
Skills needed: Biostatistics, regulatory knowledge

Financial Technology:

Salary growth: 12-18% annually
Drivers: Algorithmic trading, risk management
Skills needed: Time series analysis, real-time processing

Autonomous Vehicles:

Salary growth: 20-25% annually
Drivers: Self-driving technology, safety systems
Skills needed: Computer vision, sensor fusion

Conclusion: Next Step in Your Data Science Career

The entry-level data science job market in 2025 offers tremendous opportunities for well-prepared candidates. With salaries ranging from $60K to $165K+ and strong growth potential, data science remains one of the most promising career paths in technology.

Success in maximizing your entry-level salary comes down to three key factors: building in-demand skills, positioning yourself strategically in the market, and negotiating effectively. By focusing on these areas and staying informed about industry trends, you can position yourself for both immediate financial success and long-term career growth.

Remember that your first data science role is just the beginning. The skills you develop, relationships you build, and experience you gain will compound over time, leading to increasingly attractive opportunities and compensation packages.

Whether you're just starting your data science journey or preparing to make the leap into your first role, the market has never been more favorable for skilled, motivated candidates. The key is to approach your job search strategically, armed with the knowledge and insights shared in this guide.

Your data science career—and the salary that comes with it—is waiting. The question isn't whether you can succeed in this field, but how quickly you can position yourself to thrive in it.

Ready to start your data science career? Share this guide with other aspiring data scientists, and let us know in the comments what specific aspects of entry-level data science salaries you'd like to explore further. Don't forget to subscribe for more career insights and salary guides in the rapidly evolving world of data science!

Frequently Asked Questions

Q: What's the minimum salary I should accept for an entry-level data science position?

The minimum acceptable salary depends on your location and circumstances, but generally:

High-cost areas (SF, NYC): Don't accept below $95K
Medium-cost areas (Austin, Denver): Don't accept below $75K
Lower-cost areas (smaller cities): Don't accept below $60K

Remember to consider the complete compensation package, not just base salary.

Q: How important is a master's degree for entry-level data science salaries?

An education degree, related skills, and work experience will also influence the salary. While a master's degree can provide a slight salary advantage, practical skills and a strong portfolio are often more valuable. Many successful data scientists are self-taught or have completed intensive bootcamps.

Q: Should I prioritize salary or learning opportunities in my first data science role?

For entry-level positions, learning opportunities often provide better long-term value. A role with excellent mentorship, challenging projects, and skill development can lead to much higher salaries within 2-3 years compared to a higher-paying job with limited growth potential.

Q: How often should I expect salary increases as a data scientist?

Most companies provide annual salary reviews with increases of 3-10%. However, the biggest salary jumps typically come from:

Promotions (15-30% increases)
Job changes (20-40% increases)
Acquiring new high-value skills (10-25% increases)

Q: What skills should I focus on to maximize my entry-level salary?

Focus on these high-value skills:

Python and SQL: Essential foundations
Machine Learning: Scikit-learn, TensorFlow/PyTorch
Cloud Platforms: AWS, GCP, or Azure
Data Visualization: Tableau, Power BI, or Python libraries
Business Understanding: Domain knowledge in your target industry

Q: How do I negotiate a salary when I don't have experience?

Even without experience, you can negotiate by:

Highlighting relevant projects and skills
Demonstrating your learning ability and passion
Researching market rates thoroughly
Focusing on your unique value proposition
Considering the complete compensation package