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

Salary details

The average senior-level / expert Applied Scientist salary lies between USD 133,600 and USD 247,000 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: Jan. 19, 2025

Salary trend

Top 20 Job Tags for Senior-level / Expert Applied Scientist roles

The three most common job tag items assiciated with senior-level / expert Applied Scientist job listings are Machine Learning, Engineering 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:

Machine Learning | 760 jobs Engineering | 619 jobs Python | 599 jobs Deep Learning | 551 jobs Statistics | 514 jobs Research | 507 jobs ML models | 487 jobs LLMs | 476 jobs Computer Science | 378 jobs PhD | 316 jobs SQL | 277 jobs Testing | 259 jobs AWS | 255 jobs NLP | 247 jobs Generative AI | 225 jobs TensorFlow | 207 jobs Git | 200 jobs Java | 200 jobs Kubernetes | 198 jobs Docker | 198 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert Applied Scientist job listings are Career development, Equity / stock options and Startup environment. 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 | 729 jobs Equity / stock options | 398 jobs Startup environment | 311 jobs Conferences | 245 jobs Competitive pay | 244 jobs Health care | 236 jobs Medical leave | 232 jobs Salary bonus | 230 jobs Flex hours | 168 jobs Parental leave | 139 jobs Flex vacation | 131 jobs Team events | 130 jobs Insurance | 102 jobs 401(k) matching | 67 jobs Flexible spending account | 66 jobs Relocation support | 33 jobs Fitness / gym | 32 jobs Fertility benefits | 21 jobs Transparency | 20 jobs Wellness | 18 jobs

Salary Composition

In the United States, the salary composition for a Senior-level or Expert Applied Scientist 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 70% to 85%. Bonuses can vary significantly depending on the company and industry, often ranging from 10% to 20% of the base salary. Additional remuneration, such as stock options, can be a significant part of the package, particularly in startups or large tech firms, and can sometimes exceed the base salary in value over time.

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 market. Industry-wise, tech companies, financial services, and healthcare often offer higher compensation packages compared to academia or government roles. Company size can also influence salary composition, with larger companies typically offering more comprehensive benefits and stock options.

Increasing Salary Further

To increase your salary beyond the median of USD 160,000, consider the following strategies:

• Specialization: Develop expertise in a niche area of AI/ML, such as natural language processing, computer vision, or reinforcement learning, which can command higher salaries.
• Leadership Roles: Transition into leadership or managerial roles, such as a team lead or director, which often come with higher pay.
• Continuous Learning: Stay updated with the latest technologies and methodologies in AI/ML through courses, workshops, and conferences.
• Networking: Build a strong professional network to learn about higher-paying opportunities and gain insights into industry trends.
• Negotiation Skills: Improve your negotiation skills to better advocate for higher compensation during job offers or performance reviews.

Educational Requirements

Most Senior-level or Expert Applied Scientist positions require at least a master's degree in a relevant field such as computer science, data science, statistics, or a related discipline. A Ph.D. is often preferred, especially for roles that involve research and development of new algorithms or technologies. The educational background should include a strong foundation in mathematics, statistics, and computer programming.

Helpful Certificates

While not always mandatory, certain certifications can enhance your profile and demonstrate your commitment to the field. Some valuable certifications include:

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

These certifications can help validate your skills and knowledge in specific tools and platforms commonly used in the industry.

Required Experience

Typically, a Senior-level or Expert Applied Scientist role requires at least 5 to 10 years of experience in AI/ML or data science. This experience should include hands-on work with machine learning models, data analysis, and software development. Experience in leading projects, mentoring junior team members, and contributing to strategic decision-making is also highly valued.

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