Salary for Executive-level / Director Head of Data in United States during 2023

Salary details

The average executive-level / director Head of Data salary lies between USD 204,500 and USD 290,000 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: Nov. 24, 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 strategy. Below you find a list of the 20 most occuring job tags in 2023 and the number of open jobs that where associated with them during that period:

Engineering | 77 jobs Machine Learning | 62 jobs Data strategy | 51 jobs Python | 48 jobs SQL | 47 jobs Architecture | 42 jobs Computer Science | 39 jobs Data governance | 38 jobs Data Analytics | 37 jobs Statistics | 36 jobs Data management | 32 jobs AWS | 30 jobs Tableau | 29 jobs Security | 29 jobs Finance | 28 jobs Data quality | 28 jobs Agile | 27 jobs R | 23 jobs DataOps | 23 jobs Privacy | 23 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, Startup environment and Health care. Below you find a list of the 20 most occuring job perks or benefits in 2023 and the number of open jobs that where offering them during that period:

Career development | 107 jobs Startup environment | 65 jobs Health care | 57 jobs Flex hours | 55 jobs Equity / stock options | 43 jobs Team events | 37 jobs Flex vacation | 34 jobs Insurance | 32 jobs Parental leave | 26 jobs Competitive pay | 25 jobs Salary bonus | 25 jobs Wellness | 20 jobs Medical leave | 13 jobs Home office stipend | 12 jobs Gear | 8 jobs Conferences | 8 jobs Unlimited paid time off | 8 jobs Signing bonus | 7 jobs 401(k) matching | 5 jobs Flexible spending account | 5 jobs

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

In the United States, the salary for an executive-level position in AI/ML/Data Science typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or equity. The base salary often forms the largest portion, accounting for approximately 60-70% of the total compensation package. Performance bonuses can range from 10-20%, depending on the company's profitability and individual performance metrics. Additional remuneration, such as stock options or equity, can make up 10-30% of the total package, especially in tech companies or startups where equity is a significant incentive. The composition can vary based on the region, with tech hubs like Silicon Valley offering higher equity components, while traditional industries might focus more on base salary and bonuses. Company size also plays a role, with larger corporations offering more structured bonus plans and smaller companies potentially offering more equity.

Steps to Increase Salary from an Executive Position

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

• Expand Your Role: Take on additional responsibilities or oversee larger teams to demonstrate your capability to handle more complex challenges.
• Industry Transition: Moving to a higher-paying industry, such as finance or tech, can significantly boost your salary.
• Networking and Visibility: Increase your visibility in the industry through speaking engagements, publications, or by joining relevant professional organizations.
• Continuous Learning: Stay updated with the latest trends and technologies in AI/ML to maintain your competitive edge.
• Negotiate Effectively: Leverage your achievements and market data to negotiate better compensation during performance reviews or when offered a new position.

Educational Requirements for Executive-Level Roles

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

Helpful Certifications for AI/ML/Data Science Executives

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)
• Google Professional Machine Learning Engineer
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate

These certifications can help validate your skills and knowledge, making you a more attractive candidate for executive roles.

Experience Required for Executive-Level Positions

Typically, candidates for executive-level roles in AI/ML/Data Science are expected to have at least 10-15 years of experience in the field. This experience should include a mix of technical expertise, project management, and leadership roles. Experience in managing large teams, overseeing complex projects, and driving strategic initiatives is crucial. Additionally, a proven track record of successful AI/ML implementations and a deep understanding of industry trends are often required.

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