The salary of a Data Scientist II in the United States varies depending on several factors, including experience, location, education level, marketing trend and company sizes.
As of January 01, 2025, the average hourly pay of Data Scientist II in the United States is $47. While Salary.com is seeing that Data Scientist II salary in the US can go up to $56 or down to $37, but most earn between $42 and $52. Salary.com shows the average base salary (core compensation), as well as the average total cash compensation for the job of Data Scientist II in the United States.
| Annual Salary |
Monthly Pay |
Weekly Pay |
Hourly Wage |
|
| 75th Percentile | $107,356 | $8,946 | $2,065 | $52 |
| Average | $97,270 | $8,106 | $1,871 | $47 |
| 25th Percentile | $86,849 | $7,237 | $1,670 | $42 |
As of January 01, 2025, Salary.com is seeing that an entry-level Data Scientist II with under 1 year experience makes about $96,534. With less than 2 years of experience, a mid-level Data Scientist II makes around $96,534. After 2-4 years, the Data Scientist II pay rises to about $96,534. Those senior Data Scientist II with 5-8 years of experience earn roughly $96,534, and those Data Scientist II having 8 years or more experience is expected to earn about $96,534 on average.
| Levels | Salary |
|---|---|
| Entry Level Data Scientist II | $96,534 |
| Intermediate Level Data Scientist II | $96,534 |
| Senior Level Data Scientist II | $96,534 |
| Specialist Level Data Scientist II | $96,534 |
| Expert Level Data Scientist II | $96,534 |
| Entry Level | 1% |
| Mid Level | 1% |
| Senior Level | 1% |
| Top Level | 1% |
| Experienced | 1% |
Salaries in the United States can vary significantly from state to state due to a multitude of factors, including cost of living, local economic conditions, and industry presence.
For example, as of January 01, 2025, the average yearly salary of Data Scientist II is $107,289 in California and in Massachusetts, the average annual salary goes to $105,830. While in New Jersey, a Data Scientist II earns $106,608 per year.
What Am I Worth?
Salaries in the United States can vary significantly from city to city due to a multitude of factors, including cost of living, local economic conditions, and industry presence.
For example, as of January 01, 2025, the average yearly salary of Data Scientist II in San Francisco, CA is $121,587 and in New York, NY, the average annual salary goes to $113,611. While a Data Scientist II earns $109,039 per year in Boston, MA.
For those exploring the changing dynamics of Data Scientist II salaries, Salary.com offers detailed insights through our Job Trending in CA Labor Market analysis. As of January 01, 2025, our research highlights a notable shift in Data Scientist II compensation over the past six years. For instance, the median salary has moved from $80,112 in 2023 to about $79,998 in 2024 (for a comprehensive analysis of Data Scientist II salary trends, click here). It's crucial to consider several elements, including geographical location, experience level, industry demand, and economic development, as they play a significant role in influencing salary variations.
Salary.com job board provides millions of Data Scientist II information for you to search for. Click on search button below to see Data Scientist II job openings or enter a new job title here.
Here are companies hiring for Data Scientist II and their salaries, click below for more details.
| Core Compensation | ||||
| Core Compensation | Median | % of Total | ||
| Base Salary | $97,270 | 68.1% | ||
| Bonus | $4,954 | 3.5% | ||
| Value of Benefits | ||||
| Core Compensation | Median | % of Total | ||
| Social Security | $7,820 | 5.5% | ||
| 401K/403B | $4,293 | 3.0% | ||
| Disability | $2,044 | 1.4% | ||
| Healthcare | $8,352 | 5.8% | ||
| Pension | $5,520 | 3.9% | ||
| Time Off | $12,581 | 8.8% | ||
| Total Compensation | $142,836 | 100% | ||
Data Scientist II identifies trends, patterns, and anomalies found in big data sets by performing extensive data analysis to develop insights. Performs data mining, cleaning, and aggregation processes to prepare data, implement data models, conduct analysis, and develop databases. Being a Data Scientist II interprets results from multiple structured and unstructured data sources using programming, statistical, and analytical techniques and tools. Collaborates with teams to understand each data analysis projects' underlying purpose, focus, and objectives. Additionally, Data Scientist II designs, develops, and implements the most valuable data-driven solutions for the organization. Typically requires a master's degree in computer science, mathematics, engineering or equivalent. Typically reports to a manager. The Data Scientist II occasionally directed in several aspects of the work. Gaining exposure to some of the complex tasks within the job function. To be a Data Scientist II typically requires 2 -4 years of related experience.
Specify the abilities and skills that a person needs in order to carry out the specified job duties. Each competency has five to ten behavioral assertions that can be observed, each with a corresponding performance level (from one to five) that is required for a particular job.
Analysis: Analysis is the process of considering something carefully or using statistical methods in order to understand it or explain it.
Data Analysis: Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively. Data mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a species of unstructured data. All of the above are varieties of data analysis.
Big Data: Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many cases (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. Other concepts later attributed to big data are veracity (i.e., how much noise is in the data) and value. Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that's not the most relevant characteristic of this new data ecosystem." Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on." Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet searches, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research.
