Salary for Executive-level / Director Data Lead during 2023

Salary details

The average executive-level / director Data Lead salary lies between USD 180,000 and USD 230,000 globally. 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.

All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.

Last updated: Dec. 29, 2024

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

The salary for an Executive-level or Director Data Lead in AI/ML/Data Science typically comprises a mix of fixed salary, bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The fixed salary often forms the bulk of the compensation package, ranging from 60% to 80% of the total. 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 benefits, can make up the remaining 10% to 20%. The exact composition can vary significantly depending on the region, industry, and company size. For instance, tech companies in Silicon Valley might offer more in stock options, while financial firms in New York might provide higher cash bonuses.

Increasing Salary

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

• Expand Your Skill Set: Stay updated with the latest technologies and methodologies in AI/ML and data science. Specializing in emerging areas like deep learning, natural language processing, or AI ethics can make you more valuable.
• Leadership Development: Enhance your leadership and management skills. Pursuing executive education programs or leadership certifications can prepare you for higher roles.
• Networking: Build a strong professional network. Engaging with industry leaders and participating in conferences can open up new opportunities.
• Performance and Results: Demonstrate your ability to drive results and contribute to the company's bottom line. Documenting and communicating your achievements can position you for salary negotiations or promotions.

Educational Requirements

Most executive-level roles 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 capabilities. 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

These certifications can help validate your skills and knowledge in specific areas of AI/ML and data science.

Required Experience

Typically, a minimum of 10 to 15 years of experience in data science, machine learning, or a related field is required for an executive-level position. This experience should include a mix of technical expertise, project management, and leadership roles. Experience in leading teams, managing large-scale projects, and strategic decision-making is crucial. Additionally, experience in a specific industry, such as finance, healthcare, or technology, can be beneficial depending on the company's focus.

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