Salary for Executive-level / Director Quantitative Analyst in United States during 2024

Salary details

The average executive-level / director Quantitative Analyst salary lies between USD 95,000 and USD 200,000 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 Quantitative Analyst roles

The three most common job tag items assiciated with executive-level / director Quantitative Analyst job listings are Python, Statistics and Finance. 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:

Python | 23 jobs Statistics | 23 jobs Finance | 16 jobs Credit risk | 16 jobs Mathematics | 16 jobs Engineering | 13 jobs Testing | 13 jobs PhD | 12 jobs ML models | 12 jobs SQL | 11 jobs Research | 11 jobs Computer Science | 11 jobs Physics | 10 jobs R | 9 jobs Machine Learning | 8 jobs Economics | 7 jobs Econometrics | 7 jobs SAS | 6 jobs Data analysis | 6 jobs Probability theory | 5 jobs

Top 20 Job Perks/Benefits for Executive-level / Director Quantitative Analyst roles

The three most common job benefits and perks assiciated with executive-level / director Quantitative Analyst job listings are Career development, Health care and Competitive pay. 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 | 21 jobs Health care | 14 jobs Competitive pay | 11 jobs Medical leave | 7 jobs Insurance | 7 jobs Equity / stock options | 6 jobs Flex hours | 5 jobs Wellness | 4 jobs Salary bonus | 4 jobs Parental leave | 3 jobs 401(k) matching | 1 jobs Home office stipend | 1 jobs Paid sabbatical | 1 jobs

Salary Composition

In the United States, the salary composition for an Executive-level or Director Quantitative Analyst 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 bonuses. Smaller companies or startups might offer lower base salaries but compensate with higher equity stakes.

Increasing Salary

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

• Skill Enhancement: 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.

• 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. Engage with industry leaders, attend conferences, and participate in relevant forums to increase your visibility and opportunities.

• Performance Excellence: Consistently deliver outstanding results and demonstrate your impact on the company's bottom line. This can position you for promotions or salary negotiations.

• Explore Opportunities: Be open to opportunities in different industries or regions that may offer higher compensation packages.

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 mathematics. A Ph.D. is often preferred, especially for roles that involve significant research components or are in academia or research-focused organizations. Additionally, an MBA 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 profile and demonstrate your expertise:

• Certified Analytics Professional (CAP): Validates your ability to transform data into valuable insights.

• TensorFlow Developer Certificate: Demonstrates proficiency in building and deploying machine learning models using TensorFlow.

• AWS Certified Machine Learning – Specialty: Shows expertise in using AWS services for machine learning.

• Data Science Certifications from Coursera or edX: These can provide specialized knowledge in areas like deep learning or data engineering.

Experience Requirements

Typically, a minimum of 10-15 years of experience in quantitative analysis, data science, or a related field is required for executive-level roles. This experience should include a proven track record of leading teams, managing large-scale projects, and delivering impactful results. Experience in a specific industry, such as finance, healthcare, or technology, can also be advantageous.

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