Salary for Executive-level / Director Business Intelligence in United States during 2024

Salary details

The average executive-level / director Business Intelligence salary lies between USD 150,000 and USD 230,000 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. 22, 2024

Top 20 Job Tags for Executive-level / Director Business Intelligence roles

The three most common job tag items assiciated with executive-level / director Business Intelligence job listings are Business Intelligence, SQL and Tableau. 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:

Business Intelligence | 132 jobs SQL | 75 jobs Tableau | 67 jobs Power BI | 67 jobs Computer Science | 57 jobs Python | 54 jobs Engineering | 53 jobs Data Analytics | 52 jobs Statistics | 47 jobs Data management | 42 jobs Data visualization | 40 jobs Finance | 37 jobs Data quality | 37 jobs Data governance | 36 jobs Excel | 35 jobs Data Warehousing | 34 jobs ETL | 32 jobs Testing | 31 jobs Security | 30 jobs Privacy | 29 jobs

Top 20 Job Perks/Benefits for Executive-level / Director Business Intelligence roles

The three most common job benefits and perks assiciated with executive-level / director Business Intelligence job listings are Career development, Health care and Startup environment. 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 | 84 jobs Health care | 52 jobs Startup environment | 52 jobs Competitive pay | 32 jobs Salary bonus | 30 jobs Equity / stock options | 29 jobs Team events | 26 jobs Wellness | 25 jobs Flex hours | 24 jobs Flex vacation | 24 jobs Insurance | 21 jobs Parental leave | 20 jobs Fitness / gym | 16 jobs 401(k) matching | 14 jobs Medical leave | 13 jobs Transparency | 8 jobs Home office stipend | 5 jobs Unlimited paid time off | 3 jobs Fertility benefits | 3 jobs Travel | 2 jobs

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

In the United States, the salary composition for an Executive-level or Director of Business Intelligence role 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-wide goals, 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. Larger companies often provide more comprehensive benefits and equity options, while smaller startups might offer more significant equity stakes in lieu of higher salaries.

Steps to Increase Salary from This Position

To increase your salary further from an Executive-level position, consider the following strategies:

• Expand Your Skill Set: Continuously update your technical and leadership skills. Learning new technologies or methodologies in AI/ML can make you more valuable.
• Seek Higher Responsibility: Aim for roles with broader responsibilities, such as VP or C-level positions, which naturally come with higher compensation.
• Network and Build Relationships: Establish a strong professional network. Connections can lead to opportunities in higher-paying roles or industries.
• Negotiate Effectively: When offered a new position or during performance reviews, negotiate for higher pay, better bonuses, or more equity.
• Consider Industry Shifts: Some industries, like finance or healthcare, may offer higher salaries for similar roles due to the critical nature of data-driven decision-making.

Educational Requirements

Most Executive-level roles in AI/ML/Data Science require at least a bachelor's degree in a related field such as Computer Science, Data Science, Statistics, or Engineering. However, a master's degree or Ph.D. is often preferred, especially for roles that demand a deep understanding of complex algorithms and data analysis techniques. An MBA can also be beneficial, particularly for roles that require strong business acumen and leadership skills.

Helpful Certifications

While not always mandatory, certain certifications can enhance your qualifications and demonstrate your expertise:

• Certified Analytics Professional (CAP): Validates your ability to transform data into valuable insights.
• Google Professional Machine Learning Engineer: Demonstrates proficiency in designing, building, and productionizing ML models.
• AWS Certified Machine Learning – Specialty: Shows expertise in using AWS services for machine learning.
• Microsoft Certified: Azure AI Engineer Associate: Highlights skills in using Azure AI services.

Experience Requirements

Typically, candidates for Executive-level roles in AI/ML/Data Science need at least 10-15 years of experience in the field. This experience should include a mix of technical roles, such as data scientist or machine learning engineer, and leadership positions, such as team lead or manager. Experience in strategic decision-making, project management, and cross-functional collaboration is also crucial.

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