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

Salary details

The average executive-level / director Data Manager salary lies between USD 145,000 and USD 180,000 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. 17, 2024

Salary trend

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

The three most common job tag items assiciated with executive-level / director Data Manager job listings are Data quality, FinTech 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:

Data quality | 5 jobs FinTech | 4 jobs Data management | 4 jobs Data governance | 4 jobs KPIs | 2 jobs Tableau | 1 jobs Economics | 1 jobs Engineering | 1 jobs Data Analytics | 1 jobs Banking | 1 jobs Finance | 1 jobs Testing | 1 jobs Excel | 1 jobs Statistics | 1 jobs Monte Carlo | 1 jobs

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

The three most common job benefits and perks assiciated with executive-level / director Data Manager job listings are Equity / stock options, Parental leave and Health care. 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:

Equity / stock options | 4 jobs Parental leave | 4 jobs Health care | 4 jobs Startup environment | 4 jobs Salary bonus | 4 jobs Unlimited paid time off | 4 jobs Transparency | 1 jobs

Salary Composition

In the United States, the salary composition for an Executive-level or Director Data Manager in AI/ML/Data Science typically includes a mix of base salary, bonuses, and additional remuneration such as stock options or equity, especially in tech companies. 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 discretionary, usually account for 10% to 20%. Additional remuneration, such as stock options, equity, or profit-sharing, 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 higher equity components, while companies in the finance sector might provide more substantial bonuses.

Increasing Salary Further

To increase your salary beyond the median of USD 162,500, consider the following strategies:

• Expand Your Skill Set: Continuously update your skills in emerging AI/ML technologies and methodologies. Specializing in niche areas like deep learning, natural language processing, or AI ethics can make you more valuable.
• Pursue Advanced Education: Consider obtaining an advanced degree, such as a Ph.D. in a relevant field, which can open doors to higher-level positions and salary brackets.
• Seek Leadership Roles: Aim for roles with greater responsibility, such as VP of Data Science or Chief Data Officer, which typically come with higher compensation.
• Negotiate Effectively: Develop strong negotiation skills to better advocate for higher pay during job offers or performance reviews.
• Network Strategically: Build a robust professional network to learn about higher-paying opportunities and gain insights into industry salary trends.

Educational Requirements

Most executive-level positions 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 involve significant research or technical leadership. An MBA can also 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 qualifications and demonstrate your expertise:

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

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 AI/ML strategies, and successfully implementing data-driven solutions is crucial. Additionally, experience in a specific industry can be advantageous, as it provides domain-specific insights and knowledge.

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