Salary for Executive-level / Director Data Scientist during 2023
💰 The median Salary for Executive-level / Director Data Scientist during 2023 is USD 202,458
✏️ This salary info is based on 64 individual salaries reported during 2023
Salary details
The average executive-level / director Data Scientist salary lies between USD 157,500 and USD 249,300 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.
- Job title
- Data Scientist
- Experience
- Executive-level / Director
- Region
- global/worldwide
- Salary year
- 2023
- Sample size
- 64
- Top 10%
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- Top 25%
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- Median
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- Bottom 25%
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- Bottom 10%
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All data shown are full-time equivalent (FTE) salaries. Part-time salary information has been extrapolated to its FTE value.
Last updated:Salary trend
Top 20 Job Tags for Executive-level / Director Data Scientist roles
The three most common job tag items assiciated with executive-level / director Data Scientist job listings are Machine Learning, Python and Statistics. 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:
Machine Learning | 58 jobs Python | 49 jobs Statistics | 48 jobs Mathematics | 44 jobs Engineering | 39 jobs Research | 37 jobs Computer Science | 37 jobs SQL | 36 jobs R | 30 jobs ML models | 26 jobs PhD | 25 jobs Economics | 21 jobs Testing | 18 jobs Deep Learning | 17 jobs Spark | 17 jobs Data Analytics | 17 jobs AWS | 16 jobs Security | 16 jobs Data analysis | 16 jobs Consulting | 15 jobsTop 20 Job Perks/Benefits for Executive-level / Director Data Scientist roles
The three most common job benefits and perks assiciated with executive-level / director Data Scientist 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 2023 and the number of open jobs that where offering them during that period:
Career development | 50 jobs Health care | 31 jobs Startup environment | 30 jobs Competitive pay | 21 jobs Flex hours | 20 jobs Flex vacation | 19 jobs Insurance | 19 jobs Parental leave | 17 jobs Salary bonus | 17 jobs Equity / stock options | 16 jobs Medical leave | 15 jobs Wellness | 14 jobs 401(k) matching | 11 jobs Team events | 10 jobs Unlimited paid time off | 7 jobs Home office stipend | 6 jobs Conferences | 5 jobs Gear | 2 jobs Fitness / gym | 2 jobs Transparency | 2 jobsSalary Composition
The salary for an Executive-level or Director Data Scientist typically comprises several components: a fixed base salary, performance-based bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The composition can vary significantly depending on the region, industry, and company size.
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Region: In the United States, for instance, tech hubs like Silicon Valley or New York City often offer higher base salaries and more substantial equity packages compared to other regions. In Europe, cities like London and Berlin are known for competitive salaries, though they might offer less in terms of equity compared to the U.S.
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Industry: Industries such as finance, healthcare, and technology tend to offer higher compensation packages. In finance, bonuses can be a significant part of the total compensation, sometimes equaling or exceeding the base salary. In tech, stock options or equity can be a substantial part of the package.
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Company Size: Larger companies often provide more structured compensation packages with a clear distinction between base salary and bonuses. Startups might offer lower base salaries but compensate with higher equity stakes, which can be lucrative if the company succeeds.
Increasing Salary
To increase your salary further from an Executive-level or Director Data Scientist position, consider the following strategies:
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Expand Your Role: Take on additional responsibilities or lead larger teams. Demonstrating your ability to manage more significant projects or departments can justify a salary increase.
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Negotiate Equity: If you're in a startup or tech company, negotiating for more equity can be a way to increase your overall compensation, especially if the company is poised for growth.
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Pursue Further Education: Advanced degrees or certifications can enhance your expertise and make you more valuable to your employer.
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Network and Leverage Offers: Building a strong professional network can lead to new opportunities. Sometimes, having an offer from another company can be a powerful negotiation tool for a raise.
Educational Requirements
For an Executive-level or Director Data Scientist position, the most common educational requirement is a master's degree or Ph.D. in a relevant field such as computer science, statistics, mathematics, or engineering. These advanced degrees provide the technical foundation and analytical skills necessary for high-level data science roles. Additionally, an MBA can be beneficial for those looking to move into more strategic or business-oriented roles within data science.
Helpful Certifications
While not always required, certain certifications can be beneficial and demonstrate expertise in specific areas:
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Certified Analytics Professional (CAP): This certification is recognized across industries and validates your ability to transform data into valuable insights.
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AWS Certified Machine Learning: Useful for those working with cloud-based machine learning solutions.
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Google Professional Data Engineer: This certification is valuable for those working with Google's cloud platform and data engineering tools.
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Microsoft Certified: Azure Data Scientist Associate: Beneficial for professionals working with Microsoft's Azure platform.
Experience Requirements
Typically, a Director Data Scientist role requires extensive experience, often 10+ years in data science or related fields. This experience should include:
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Leadership Experience: Proven track record of leading data science teams and projects.
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Technical Expertise: Deep understanding of machine learning algorithms, statistical analysis, and data engineering.
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Industry Knowledge: Experience in the specific industry of the company, whether it's finance, healthcare, tech, etc.
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