Salary for Executive-level / Director Head of Data in United States during 2024

Salary details

The average executive-level / director Head of Data salary lies between USD 200,000 and USD 269,000 in the United States. It represents the overall compensation/gross salary amount for the working year (before deductions like social security, taxes and other contributions), not including equity/stock options or similar benefits.

Region represents the primary country of residence of an employee during the year (or residence for tax purposes). All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.

Last updated: Dec. 15, 2024

Salary trend

Top 20 Job Tags for Executive-level / Director Head of Data roles

The three most common job tag items assiciated with executive-level / director Head of Data job listings are Engineering, Machine Learning and Data management. Below you find a list of the 20 most occuring job tags in 2024 and the number of open jobs that where associated with them during that period:

Engineering | 211 jobs Machine Learning | 152 jobs Data management | 151 jobs Data strategy | 144 jobs Data governance | 137 jobs Architecture | 133 jobs SQL | 125 jobs Security | 119 jobs Python | 118 jobs Computer Science | 111 jobs Statistics | 109 jobs Data Analytics | 104 jobs Data quality | 103 jobs Research | 92 jobs Privacy | 89 jobs Agile | 81 jobs R | 80 jobs AWS | 75 jobs Finance | 73 jobs Pipelines | 62 jobs

Top 20 Job Perks/Benefits for Executive-level / Director Head of Data roles

The three most common job benefits and perks assiciated with executive-level / director Head of Data job listings are Career development, Health care and Flex hours. Below you find a list of the 20 most occuring job perks or benefits in 2024 and the number of open jobs that where offering them during that period:

Career development | 289 jobs Health care | 139 jobs Flex hours | 127 jobs Startup environment | 126 jobs Competitive pay | 82 jobs Team events | 79 jobs Equity / stock options | 76 jobs Insurance | 65 jobs Parental leave | 54 jobs Salary bonus | 52 jobs Flex vacation | 45 jobs Medical leave | 37 jobs 401(k) matching | 31 jobs Wellness | 30 jobs Transparency | 26 jobs Unlimited paid time off | 19 jobs Home office stipend | 18 jobs Conferences | 17 jobs Travel | 16 jobs Fitness / gym | 13 jobs

Salary Composition for Executive-Level AI/ML/Data Science Roles

In the United States, the salary composition for executive-level roles in AI/ML/Data Science typically includes a mix of base salary, bonuses, and additional remuneration such as stock options or equity. The base salary often constitutes the largest portion, ranging from 60% to 80% of the total compensation package. Bonuses, which can be performance-based or tied to company metrics, usually account for 10% to 20%. Additional remuneration, such as stock options, equity, or other long-term incentives, can make up the remaining 10% to 20%.

The composition can vary significantly depending on the region, industry, and company size. For instance, tech hubs like Silicon Valley or New York may offer higher base salaries and more substantial equity packages. In contrast, companies in the Midwest might offer lower base salaries but compensate with higher bonuses. Industries such as finance or healthcare may provide more lucrative bonuses compared to traditional tech companies.

Steps to Increase Salary from an Executive Position

To increase your salary further from an executive position, consider the following strategies:

• Expand Your Network: Building relationships with other executives and industry leaders can open doors to higher-paying opportunities.
• Enhance Your Skills: Stay updated with the latest trends and technologies in AI/ML and data science. Consider learning about emerging fields like quantum computing or advanced AI ethics.
• Seek Leadership Roles in Larger Organizations: Larger companies often have bigger budgets for executive compensation.
• Negotiate Equity and Bonuses: Focus on negotiating better equity packages or performance-based bonuses, which can significantly increase your total compensation.
• Pursue Board Memberships: Serving on the board of directors for other companies can provide additional income and enhance your professional reputation.

Educational Requirements for Executive-Level Roles

Most executive-level positions in AI/ML/Data Science require at least a master's degree in a related field such as computer science, data science, statistics, or engineering. A Ph.D. can be advantageous, especially for roles that require deep technical expertise or are research-focused. Additionally, an MBA or a degree in business administration can be beneficial for roles that require strong business acumen and leadership skills.

Helpful Certifications for Executive Roles

While not always mandatory, certain certifications can enhance your credibility and demonstrate your commitment to the field. Some valuable certifications include:

• Certified Analytics Professional (CAP): Validates your ability to transform data into valuable insights.
• AWS Certified Machine Learning – Specialty: Demonstrates expertise in building, training, and deploying machine learning models on AWS.
• Google Professional Machine Learning Engineer: Showcases your ability to design, build, and productionize ML models on Google Cloud.
• Data Science Council of America (DASCA) Certifications: Offers various levels of data science certifications that are recognized globally.

Experience Required for Executive-Level Roles

Typically, executive-level roles require at least 10 to 15 years of experience in the field, with a significant portion of that time spent in leadership or managerial positions. Experience in leading large teams, managing complex projects, and driving strategic initiatives is crucial. Additionally, a proven track record of delivering business value through data-driven decision-making and innovation is often required.

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