Salary for Senior-level / Expert Manager in United States during 2024

Salary details

The average senior-level / expert Manager salary lies between USD 141,100 and USD 224,000 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. 3, 2024

Top 20 Job Tags for Senior-level / Expert Manager roles

The three most common job tag items assiciated with senior-level / expert Manager job listings are Engineering, Machine Learning and Python. 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:

Engineering | 3141 jobs Machine Learning | 2435 jobs Python | 2188 jobs Computer Science | 1985 jobs SQL | 1832 jobs Research | 1632 jobs Statistics | 1442 jobs Architecture | 1311 jobs Agile | 1245 jobs Security | 1127 jobs Testing | 1080 jobs AWS | 1041 jobs Data management | 1034 jobs R | 1022 jobs Data Analytics | 991 jobs Privacy | 940 jobs Mathematics | 912 jobs Azure | 836 jobs Data analysis | 820 jobs Finance | 778 jobs

Top 20 Job Perks/Benefits for Senior-level / Expert Manager roles

The three most common job benefits and perks assiciated with senior-level / expert Manager job listings are Career development, Health care and Equity / stock options. 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 | 3644 jobs Health care | 2067 jobs Equity / stock options | 1444 jobs Flex hours | 1290 jobs Startup environment | 1258 jobs Competitive pay | 1188 jobs Salary bonus | 1061 jobs Team events | 1029 jobs Medical leave | 881 jobs Insurance | 874 jobs Flex vacation | 849 jobs Parental leave | 786 jobs Wellness | 529 jobs 401(k) matching | 515 jobs Transparency | 257 jobs Home office stipend | 217 jobs Flexible spending account | 181 jobs Relocation support | 178 jobs Conferences | 175 jobs Fitness / gym | 171 jobs

Salary Composition

In the United States, the salary composition for a Senior-level or Expert Manager in AI/ML/Data Science typically includes a combination of a fixed base salary, performance-based bonuses, and additional remuneration such as stock options or equity, especially in tech companies. The base salary often constitutes the majority of the total compensation package, ranging from 60% to 80%. Bonuses can vary significantly depending on the company's performance and individual achievements, usually accounting for 10% to 20% of the total compensation. Additional remuneration, such as stock options, can be a significant part of the package, particularly in startups or large tech firms, and may range from 10% to 30%.

Regional differences also play a role; for instance, salaries in tech hubs like San Francisco or New York City tend to be higher due to the cost of living and competitive job markets. Industry variations are notable as well, with finance and tech sectors often offering higher compensation compared to academia or non-profit organizations. Company size can influence salary composition, with larger companies typically providing more comprehensive benefits and stock options.

Increasing Salary

To increase your salary further from a Senior-level or Expert Manager position, consider the following strategies:

• Expand Your Skill Set: Continuously update your technical skills and stay abreast of the latest trends in AI/ML. Specializing in emerging areas like deep learning, natural language processing, or AI ethics can make you more valuable.

• Leadership and Management Skills: Enhance your leadership capabilities by taking on more responsibilities, leading larger teams, or managing cross-functional projects. Pursuing executive education or leadership training can also be beneficial.

• Networking and Industry Engagement: Build a strong professional network by attending industry conferences, participating in workshops, and engaging with online communities. This can open up opportunities for higher-paying roles.

• Negotiate Effectively: When discussing compensation, be prepared to negotiate based on your contributions, market research, and the value you bring to the organization.

• Consider Relocation: If feasible, relocating to a region with higher salary benchmarks for your role can lead to a significant pay increase.

Educational Requirements

For a Senior-level or Expert Manager role in AI/ML/Data Science, a strong educational background is typically required. Most candidates hold 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, particularly for roles that involve research or advanced technical problem-solving. Additionally, a solid foundation in mathematics and programming is essential.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credentials and demonstrate expertise. Some valuable certifications include:

• Certified Data Scientist (CDS)
• TensorFlow Developer Certificate
• AWS Certified Machine Learning – Specialty
• Microsoft Certified: Azure AI Engineer Associate
• Google Professional Machine Learning Engineer

These certifications can validate your skills in specific tools and platforms, making you more attractive to potential employers.

Required Experience

Typically, a Senior-level or Expert Manager in AI/ML/Data Science is expected to have at least 8-10 years of relevant experience. This includes hands-on experience in data analysis, machine learning model development, and project management. Experience leading teams and managing complex projects is crucial, as is a proven track record of delivering successful AI/ML solutions.

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