The salary of a Data Science Director in the United States varies depending on several factors, including experience, location, education level, marketing trend and company sizes.
As of April 01, 2025, the average annual salary for a Data Science Director in the United States is $193,270. According to Salary.com, salaries can range from a low of $162,046 to a high of $228,126, with most professionals earning between $176,926 and $211,515.
| Annual Salary |
Monthly Pay |
Weekly Pay |
Hourly Wage |
|
| 75th Percentile | $211,515 | $17,626 | $4,068 | $102 |
| Average | $193,270 | $16,106 | $3,717 | $93 |
| 25th Percentile | $176,926 | $14,744 | $3,402 | $85 |
An entry-level Data Science Director with less than 1 year of experience earns about $183,268. With 1-2 years of experience, the average salary increases to $184,732. For 2-4 years of experience, the pay typically rises to $186,684. Senior-level professionals with 5-8 years of experience earn around $189,611, and those with over 8 years of experience can expect an average of $193,270.
| Levels | Salary |
|---|---|
| Entry Level Data Science Director | $183,268 |
| Intermediate Level Data Science Director | $184,732 |
| Senior Level Data Science Director | $186,684 |
| Specialist Level Data Science Director | $189,611 |
| Expert Level Data Science Director | $193,270 |
| Entry Level | 5% |
| Mid Level | 4% |
| Senior Level | 3% |
| Top Level | 2% |
| Experienced | 0% |
Salaries in the United States differ widely between states due to factors like cost of living, local economies, and industry presence.
What Am I Worth?
Salaries in the United States can vary greatly between cities due to factors like cost of living, local economies, and industry presence.
As of April 01, 2025, our research reveals a significant shift in Data Science Director compensation over the past six years. For example, the median salary increased from $187,987 in 2023 to approximately $188,156 in 2024. (For a detailed analysis of Data Science Director salary trends, click here.)
Key factors like location, experience, industry demand, and economic growth significantly influence salary variations, making them important to consider.
Salary.com job board provides millions of Data Science Director information for you to search for. Click on search button below to see Data Science Director job openings or enter a new job title here.
Here are companies hiring for Data Science Director and their salaries, click below for more details.
| Core Compensation | ||||
| Core Compensation | Median | % of Total | ||
| Base Salary | $193,270 | 74.0% | ||
| Bonus | $0 | 0.0% | ||
| Value of Benefits | ||||
| Core Compensation | Median | % of Total | ||
| Social Security | $13,256 | 5.1% | ||
| 401K/403B | $8,117 | 3.1% | ||
| Disability | $3,865 | 1.5% | ||
| Healthcare | $8,352 | 3.2% | ||
| Pension | $10,437 | 4.0% | ||
| Time Off | $23,787 | 9.1% | ||
| Total Compensation | $261,084 | 100% | ||
The average annual salary of Data Science Director is $193,270. In case you are finding an easy salary calculator, the average hourly pay of Data Science Director is $93; the average weekly pay of Data Science Director is $3,717; the average monthly pay of Data Science Director is $16,106.
A Data Science Director's earning potential can vary widely depending on several factors, including location, industry, experience, education, and the specific employer. According to the latest salary data by Salary.com, a Data Science Director earns the most in San Jose, CA, where the annual salary of a Data Science Director is $243,771.
The highest pay for Data Science Director is $228,126.
The lowest pay for Data Science Director is $162,046.
Establishes, plans, and administers the overall policies and goals of the data science function. Provides strategic guidance and overall direction for analytical efforts. Determines the appropriate tools, techniques, staffing and methodologies to extract data that produces meaningful results. Uses extensive knowledge and research into big data tools to guide the integration of new and existing tools into the organization's data science tech stack. Typically requires a master's degree in computer science, mathematics, engineering or equivalent. Typically reports to top management. Manages a departmental sub-function within a broader departmental function. Creates functional strategies and specific objectives for the sub-function and develops budgets/policies/procedures to support the functional infrastructure. Typically requires 5+ years of managerial experience. Deep knowledge of the managed sub-function and solid knowledge of the overall departmental function.
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.
Leadership: Knowledge of and ability to employ effective strategies that motivate and guide other members within our business to achieve optimum results.
Data Analytics: 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.
Python: Applying the concepts and algorithms of Python to design, develop and maintain software applications to comply with business requirements.
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