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

Salary details

The average senior-level / expert Data Analyst salary lies between USD 99,200 and USD 158,400 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. 24, 2024

Salary trend

Top 20 Job Tags for Senior-level / Expert Data Analyst roles

The three most common job tag items assiciated with senior-level / expert Data Analyst job listings are SQL, Python and Statistics. 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:

SQL | 3386 jobs Python | 2441 jobs Statistics | 2141 jobs Tableau | 2096 jobs Data analysis | 1983 jobs Engineering | 1743 jobs Excel | 1473 jobs Power BI | 1442 jobs Data Analytics | 1369 jobs R | 1271 jobs Data visualization | 1236 jobs Computer Science | 1133 jobs Finance | 1070 jobs Mathematics | 1047 jobs Research | 1037 jobs Testing | 1024 jobs Data quality | 929 jobs Business Intelligence | 900 jobs Machine Learning | 759 jobs Looker | 731 jobs

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

The three most common job benefits and perks assiciated with senior-level / expert Data Analyst job listings are Career development, Health care and Flex hours. 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 | 2592 jobs Health care | 1805 jobs Flex hours | 1274 jobs Competitive pay | 1030 jobs Equity / stock options | 1005 jobs Startup environment | 989 jobs Team events | 909 jobs Flex vacation | 852 jobs Insurance | 834 jobs Parental leave | 728 jobs Salary bonus | 708 jobs Medical leave | 660 jobs Wellness | 514 jobs 401(k) matching | 503 jobs Fitness / gym | 214 jobs Transparency | 201 jobs Home office stipend | 179 jobs Unlimited paid time off | 154 jobs Gear | 152 jobs Flexible spending account | 117 jobs

Salary Composition

In the United States, the salary composition for a Senior-level or Expert Data Analyst in AI/ML/Data Science typically includes a combination of a fixed base salary, performance bonuses, and additional remuneration such as stock options or profit-sharing. The base salary often constitutes the majority of the total compensation package, ranging from 70% to 85%. Performance bonuses can vary significantly, often comprising 10% to 20% of the total compensation, depending on individual and company performance. Additional remuneration, such as stock options, is more common in tech companies and startups, potentially making up 5% to 10% of the total package.

Regional differences can also impact salary composition. For instance, tech hubs like San Francisco or New York may offer higher base salaries and more substantial stock options due to the competitive job market. Industry also plays a role; tech and finance sectors typically offer higher compensation packages compared to healthcare or education. Company size can influence the structure as well, with larger companies often providing more comprehensive benefits and bonuses.

Increasing Salary

To increase your salary further from a Senior-level Data Analyst position, consider the following strategies:

• Specialization: Develop expertise in a niche area within AI/ML, such as natural language processing or computer vision, which can command higher salaries.
• Leadership Roles: Transition into managerial or leadership roles, such as a Data Science Manager or Director of Analytics, which typically offer higher compensation.
• Continuous Learning: Stay updated with the latest technologies and methodologies in AI/ML, and consider pursuing advanced certifications or degrees.
• Networking: Engage with professional networks and industry events to explore new opportunities and gain insights into market trends.
• Negotiation Skills: Enhance your negotiation skills to better advocate for salary increases during performance reviews or when switching jobs.

Educational Requirements

Most Senior-level Data Analyst positions in AI/ML/Data Science require at least a bachelor's degree in a related field such as Computer Science, Statistics, Mathematics, or Engineering. However, a master's degree or Ph.D. is often preferred, especially for roles that involve complex data modeling and machine learning tasks. Advanced degrees can provide a deeper understanding of theoretical concepts and practical applications, making candidates more competitive in the job market.

Helpful Certifications

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

• Certified Analytics Professional (CAP): Validates your ability to transform data into valuable insights.
• Google Professional Data Engineer: Demonstrates proficiency in designing and managing data processing systems.
• Microsoft Certified: Azure Data Scientist Associate: Focuses on implementing and running machine learning workloads on Azure.
• AWS Certified Machine Learning – Specialty: Highlights your skills in building, training, and deploying machine learning models on AWS.

Required Experience

Typically, a Senior-level Data Analyst role requires 5 to 10 years of experience in data analysis, with a strong background in statistical analysis, data visualization, and machine learning techniques. Experience in handling large datasets, proficiency in programming languages like Python or R, and familiarity with data manipulation tools such as SQL are often essential. Additionally, experience in leading projects or teams can be advantageous for senior roles.

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