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

Salary details

The average executive-level / director Data Lead salary lies between USD 180,000 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: Jan. 5, 2025

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

The three most common job tag items assiciated with executive-level / director Data Lead job listings are Statistics, Python and SQL. 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:

Statistics | 9 jobs Python | 8 jobs SQL | 8 jobs Data visualization | 8 jobs Data analysis | 8 jobs Computer Science | 8 jobs R | 7 jobs Machine Learning | 7 jobs AWS | 7 jobs Agile | 7 jobs Statistical modeling | 7 jobs Predictive modeling | 7 jobs Privacy | 7 jobs Economics | 4 jobs Tableau | 3 jobs Engineering | 2 jobs Data Analytics | 2 jobs Power BI | 2 jobs Data strategy | 2 jobs Jira | 2 jobs

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

The three most common job benefits and perks assiciated with executive-level / director Data Lead job listings are Startup environment, Career development 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:

Startup environment | 10 jobs Career development | 8 jobs Health care | 7 jobs Equity / stock options | 1 jobs Wellness | 1 jobs Fitness / gym | 1 jobs Team events | 1 jobs Relocation support | 1 jobs Snacks / Drinks | 1 jobs Yoga | 1 jobs

Salary Composition

In the United States, the salary composition for an Executive-level or Director Data Lead 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 profits, 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 companies in Silicon Valley might offer higher equity components, while financial firms in New York may provide more substantial cash bonuses. Smaller startups might offer lower base salaries but compensate with significant equity stakes, whereas larger corporations might provide more stable and predictable compensation packages.

Increasing Salary Further

To increase your salary beyond the median of USD 226,000, consider the following strategies:

• Expand Your Skill Set: Continuously update your technical and leadership skills. Specializing in emerging technologies or methodologies can make you more valuable.
• Network and Build Relationships: Engage with industry leaders and peers. Networking can open doors to higher-paying opportunities.
• Seek Leadership Roles: Aim for roles with greater responsibility, such as VP or C-level positions, which typically offer higher compensation.
• Negotiate Effectively: When offered a new position or during performance reviews, negotiate for higher pay, better bonuses, or more equity.
• Consider Relocation: Some regions offer higher salaries due to cost of living or demand for talent. Be open to relocating if it aligns with your career goals.

Educational Requirements

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 Engineering. A Ph.D. can be advantageous, especially for roles that require deep technical expertise or research experience. Additionally, an MBA or a degree in management can be beneficial for those looking to move into more strategic or business-oriented roles.

Helpful Certifications

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
• Data Science Council of America (DASCA) Senior Data Scientist

These certifications can help validate your skills and knowledge, making you a more attractive candidate for higher-level positions.

Required Experience

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 expertise, project management, and leadership roles. Experience in managing teams, developing and implementing data strategies, and driving business outcomes through data-driven insights is crucial. Additionally, experience in a specific industry can be beneficial, as it provides domain knowledge that can be leveraged in strategic decision-making.

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