Salary for Senior-level / Expert Data Scientist in United States during 2023

Salary details

The average senior-level / expert Data Scientist salary lies between USD 136,000 and USD 207,000 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 Senior-level / Expert Data Scientist roles

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

Python | 2586 jobs Machine Learning | 2319 jobs Statistics | 2213 jobs SQL | 2077 jobs Engineering | 1924 jobs Computer Science | 1387 jobs R | 1287 jobs Mathematics | 1284 jobs Research | 1226 jobs ML models | 947 jobs Testing | 761 jobs Spark | 696 jobs Data analysis | 680 jobs Tableau | 658 jobs AWS | 646 jobs Deep Learning | 604 jobs PhD | 588 jobs Big Data | 567 jobs Pipelines | 564 jobs Economics | 559 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert Data Scientist 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 2023 and the number of open jobs that where offering them during that period:

Career development | 2154 jobs Health care | 1045 jobs Equity / stock options | 815 jobs Startup environment | 798 jobs Flex hours | 776 jobs Competitive pay | 694 jobs Team events | 671 jobs Flex vacation | 653 jobs Salary bonus | 563 jobs Parental leave | 552 jobs Insurance | 533 jobs Medical leave | 381 jobs Wellness | 369 jobs 401(k) matching | 329 jobs Home office stipend | 260 jobs Unlimited paid time off | 229 jobs Conferences | 144 jobs Fitness / gym | 119 jobs Relocation support | 96 jobs Gear | 91 jobs

Salary Composition

In the United States, the salary composition for a Senior-level or Expert Data Scientist 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 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 in tech startups or large tech firms, sometimes accounting for 10% to 30% of the total compensation.

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-wise, sectors like finance, technology, and healthcare often offer higher compensation compared to academia or non-profit organizations. 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 current median, consider the following strategies:

• Specialization: Develop expertise in a niche area of data science, such as deep learning, natural language processing, or AI ethics, which can make you more valuable to employers.
• Leadership Roles: Transition into leadership or managerial roles, such as a Data Science Manager or Director of Data Science, which typically come with higher compensation.
• Continuous Learning: Stay updated with the latest tools and technologies in AI/ML, and consider pursuing advanced certifications or courses to enhance your skill set.
• Networking: Build a strong professional network by attending industry conferences, participating in online forums, and engaging with thought leaders in the field.
• Negotiation Skills: Improve your negotiation skills to better advocate for higher compensation during job offers or performance reviews.

Educational Requirements

Most Senior-level Data Scientist positions require at least a Master's degree in a relevant field such as Computer Science, Statistics, Mathematics, or Data Science. A Ph.D. is often preferred, especially for roles that involve research or advanced algorithm development. The educational background should provide a strong foundation in statistical analysis, machine learning, and programming.

Helpful Certifications

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

• Certified Data Scientist (CDS)
• Microsoft Certified: Azure Data Scientist Associate
• Google Professional Data Engineer
• IBM Data Science Professional Certificate
• TensorFlow Developer Certificate

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

Required Experience

Typically, a Senior-level Data Scientist is expected to have at least 5 to 10 years of experience in data science or related fields. This experience should include hands-on work with data analysis, machine learning model development, and deployment. Experience in leading projects, mentoring junior data scientists, and collaborating with cross-functional teams is also highly valued.

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