The role of a data scientist is consistently ranked as one of the most promising and lucrative careers of the 21st century. For those starting their professional journey, the field offers not just intellectual stimulation but also significant financial rewards. If you're wondering what to expect, you're in the right place. An entry-level data scientist in the United States can anticipate a starting salary that often ranges from $90,000 to over $120,000 per year.
This article will break down what an entry-level data scientist does, the average salary you can expect, and the crucial factors that can significantly impact your earning potential.
What Does an Entry-Level Data Scientist Do?
Before diving into the numbers, it's essential to understand the role. An entry-level or junior data scientist is a professional who helps organizations make sense of vast amounts of data. While a senior data scientist might be responsible for designing complex machine learning systems or setting data strategy, a junior professional typically works under their guidance, focusing on more foundational tasks.
Key responsibilities often include:
- Data Cleaning and Preprocessing: Sourcing, gathering, and cleaning datasets to ensure they are accurate and ready for analysis. This is a critical, and often time-consuming, part of the job.
- Exploratory Data Analysis (EDA): Using statistical techniques and visualization tools to identify trends, patterns, and anomalies in the data.
- Model Building and Implementation: Assisting in building and training basic predictive models to answer specific business questions.
- Visualization and Reporting: Creating dashboards, charts, and reports to communicate findings to stakeholders, both technical and non-technical.
- Supporting Senior Staff: Collaborating with senior data scientists, data engineers, and analysts on larger, more complex projects.
Average Entry-Level Data Scientist Salary
Salary data can vary based on the source, but by looking at several authoritative aggregators, we can establish a reliable baseline. For professionals with 0-2 years of experience, the financial outlook is very strong.
- Average Base Salary: According to Salary.com, the typical salary range for a Data Scientist I (an entry-level position) in the U.S. falls between $95,901 and $118,701, with the average being around $107,055 as of late 2023.
- Total Compensation: Glassdoor reports a similar national average base pay of $103,500 per year for an entry-level data scientist. It's important to note this often doesn't include additional compensation like cash bonuses or stock options, which can add several thousand dollars to the total package, especially in the tech industry.
- Overall Career Trajectory: For context, the U.S. Bureau of Labor Statistics (BLS) reports the median annual wage for all data scientists was $136,620 in May 2023. This figure includes professionals at all experience levels, highlighting the significant room for salary growth as you gain expertise.
These figures confirm that even at the start of your career, data science is a financially rewarding path. However, these numbers are just an average. Your specific salary will be influenced by several key factors.
Key Factors That Influence Salary
Where you work, what you know, and who you work for will play a massive role in your starting salary. Let's explore the most impactful factors.
### Level of Education
Your academic background is a primary determinant of your starting salary. While a bachelor's degree is often the minimum requirement, advanced degrees can unlock higher earning potential from day one.
- Bachelor's Degree: A bachelor's in a quantitative field like Computer Science, Statistics, Mathematics, or Economics is the standard entry point. It qualifies you for most junior roles.
- Master's Degree: A master's degree is highly valued and often preferred by employers. It signals a deeper, more specialized knowledge base. According to data from Payscale, professionals with a Master of Science (MS) in Data Science can earn a higher starting salary than their bachelor's-level counterparts.
- Ph.D.: A doctorate often leads to the highest starting salaries, particularly for roles in research and development (R&D) or highly specialized areas like deep learning or natural language processing. These candidates can tackle more complex, theoretical problems, which commands a premium.
### Years of Experience
While this article focuses on entry-level roles (0-2 years), it's crucial to understand how quickly your salary can grow. Experience is the single most powerful driver of salary increases in this field.
- Entry-Level (0-2 years): This is the baseline we've been discussing, typically in the $95k - $120k range.
- Mid-Level (3-5 years): After a few years of hands-on experience, data scientists can see a significant jump in salary, often moving into the $130,000 - $160,000+ range.
- Senior/Lead (5+ years): With substantial experience, senior and lead data scientists can command salaries well over $170,000, with top-tier talent at major tech companies earning upwards of $250,000 or more with total compensation.
### Geographic Location
Where you live and work matters—a lot. Tech hubs and major metropolitan areas with a high cost of living and high demand for talent offer significantly higher salaries. The rise of remote work has slightly altered this dynamic, but location remains a top factor.
Here’s a look at how salaries can vary by location, according to Salary.com and Glassdoor data:
| City/Region | Typical Entry-Level Salary Range | Notes |
| :--- | :--- | :--- |
| San Francisco Bay Area, CA | $120,000 - $150,000+ | The epicenter of the tech industry; highest salaries but also the highest cost of living. |
| Seattle, WA | $110,000 - $135,000 | Home to Amazon, Microsoft, and a thriving tech scene. |
| New York, NY | $105,000 - $130,000 | A major hub for finance, media, and tech, all of which hire data scientists. |
| Boston, MA | $100,000 - $125,000 | Strong in biotech, healthcare, and technology. |
| Austin, TX | $95,000 - $115,000 | A rapidly growing tech hub with a lower cost of living than coastal cities. |
| Chicago, IL | $90,000 - $110,000 | A diverse economy with opportunities in finance, logistics, and retail. |
### Company Type
The type of company you work for dramatically affects your compensation structure.
- Big Tech (FAANG/MANGA): Companies like Google, Meta, Amazon, Apple, and Netflix are known for offering the highest compensation packages, which include a strong base salary plus significant stock options (RSUs) and annual bonuses.
- Tech Startups: Well-funded startups can be very competitive, but the compensation mix may lean more heavily on equity (stock options) than a high base salary. This offers high risk but also high potential reward.
- Established Non-Tech Corporations: Large companies in finance, healthcare, retail, and manufacturing are increasingly hiring data scientists. Their base salaries are competitive, though their bonus and stock structures may be less generous than in Big Tech.
- Consulting Firms: Consulting offers a chance to work on diverse projects across various industries. Salaries are often strong, but the work-life balance can be more demanding.
- Government and Academia: These sectors typically offer the lowest base salaries. However, they compensate with excellent job security, robust benefits, and a better work-life balance.
### Area of Specialization
As you develop your skills, specializing in a high-demand niche can make you a more valuable—and higher-paid—candidate. Some of the most lucrative specializations include:
- Deep Learning / Artificial Intelligence: Experts in building and deploying neural networks for tasks like image recognition or AI-driven products.
- Natural Language Processing (NLP): Professionals who create systems that can understand and process human language, powering everything from chatbots to sentiment analysis.
- MLOps (Machine Learning Operations): A hybrid role focused on the deployment, monitoring, and maintenance of machine learning models in production, bridging the gap between data science and DevOps.
- Computer Vision: A specialized field of AI that trains computers to interpret and understand the visual world.
Job Outlook
The future for data scientists is exceptionally bright. According to the U.S. Bureau of Labor Statistics (BLS), employment for data scientists is projected to grow 35 percent from 2022 to 2032. This is much faster than the average for all occupations.
This incredible growth rate is fueled by the simple fact that organizations across every sector are collecting more data than ever before. They need skilled professionals who can turn that raw data into a strategic asset, driving efficiency, innovation, and profitability.
Conclusion
Embarking on a career as a data scientist is a wise choice for those with a passion for problem-solving and a quantitative mindset. The financial rewards are clear, with entry-level salaries routinely breaking the six-figure mark.
Here are the key takeaways for any aspiring data scientist:
1. Expect a Strong Start: A competitive entry-level salary is the norm, not the exception.
2. Education is Your Foundation: A master's degree can give you a significant edge and a higher starting salary.
3. Location is a Key Lever: Your earning potential is directly tied to the job market in your city or region.
4. Think Beyond Base Salary: Consider the entire compensation package, including bonuses and stock, especially when evaluating offers from tech companies.
5. Growth is Rapid: Your salary potential will increase substantially within just a few years of gaining practical experience.
While the numbers are compelling, the true value of a data science career lies in its potential for impact. By harnessing the power of data, you can help solve some of the world's most challenging problems. By strategically building your skills and targeting the right opportunities, you are well-positioned to launch a career that is not only lucrative but also deeply rewarding.