Data Specialist Salary in United States during 2024

Salary details

The average Data Specialist salary lies between USD 60,000 and USD 97,000 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: Dec. 15, 2024

Salary trend

Top 20 Job Tags for Data Specialist roles

The three most common job tag items assiciated with Data Specialist job listings are Excel, Data management and Research. 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:

Excel | 618 jobs Data management | 459 jobs Research | 405 jobs Data quality | 374 jobs SQL | 355 jobs Data analysis | 297 jobs Engineering | 257 jobs Power BI | 254 jobs Finance | 250 jobs Testing | 241 jobs Python | 214 jobs Privacy | 213 jobs Security | 207 jobs Computer Science | 171 jobs Statistics | 161 jobs R | 156 jobs Data governance | 148 jobs Tableau | 146 jobs Data visualization | 135 jobs Banking | 125 jobs

Top 20 Job Perks/Benefits for Data Specialist roles

The three most common job benefits and perks assiciated with Data Specialist 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 | 698 jobs Health care | 442 jobs Flex hours | 360 jobs Startup environment | 289 jobs Competitive pay | 252 jobs Team events | 233 jobs Equity / stock options | 183 jobs Insurance | 180 jobs Flex vacation | 171 jobs Salary bonus | 165 jobs Medical leave | 110 jobs Wellness | 107 jobs Parental leave | 104 jobs Relocation support | 69 jobs 401(k) matching | 67 jobs Transparency | 38 jobs Home office stipend | 37 jobs Gear | 36 jobs Fitness / gym | 31 jobs Conferences | 27 jobs

Salary Composition for a Data Specialist in AI/ML/Data Science

The salary composition for a Data Specialist in AI/ML/Data Science typically includes a fixed base salary, performance bonuses, and additional remuneration such as stock options or benefits. The base salary often constitutes the majority of the total compensation package, ranging from 70% to 85%. Performance bonuses can vary significantly depending on the company and industry, often ranging from 5% to 20% of the base salary. Additional remuneration, such as stock options, profit-sharing, or comprehensive benefits packages, can also play a role, especially in larger tech companies or startups.

Regional differences are notable, with tech hubs like San Francisco, New York, and Seattle offering higher salaries due to the cost of living and demand for talent. Industry also plays a role; for instance, finance and tech industries tend to offer higher compensation compared to education or non-profit sectors. Company size can influence salary composition as well, with larger companies often providing more comprehensive benefits and bonuses.

Steps to Increase Salary from a Data Specialist Position

To increase your salary from a Data Specialist position, consider the following strategies:

• Skill Enhancement: Continuously update and expand your technical skills, particularly in emerging AI/ML technologies and tools. Proficiency in programming languages like Python, R, and SQL, as well as experience with machine learning frameworks such as TensorFlow or PyTorch, can be advantageous.

• Advanced Education: Pursuing a master's degree or specialized certifications in data science or machine learning can make you more competitive and open up higher-paying opportunities.

• Networking and Professional Development: Engage in networking opportunities, attend industry conferences, and participate in workshops to connect with professionals and stay informed about industry trends.

• Performance and Leadership: Demonstrate strong performance in your current role and seek leadership opportunities or projects that showcase your ability to drive results and manage teams.

• Negotiation Skills: Develop strong negotiation skills to effectively advocate for salary increases during performance reviews or when considering new job offers.

Educational Requirements for a Data Specialist Role

Most Data Specialist roles in AI/ML/Data Science require at least a bachelor's degree in a related field such as computer science, data science, statistics, mathematics, or engineering. A strong foundation in these areas is essential for understanding data analysis, machine learning algorithms, and statistical methods. Some positions may prefer or require a master's degree, especially for more advanced roles or in competitive job markets.

Helpful Certifications for Data Specialists

Several certifications can enhance your qualifications and demonstrate expertise in data science and machine learning:

• Certified Data Scientist (CDS): Offered by various organizations, this certification validates your skills in data analysis, machine learning, and data visualization.

• Google Professional Data Engineer: This certification focuses on designing, building, and operationalizing data processing systems on Google Cloud Platform.

• Microsoft Certified: Azure Data Scientist Associate: This certification demonstrates your ability to apply machine learning techniques on Azure.

• AWS Certified Machine Learning – Specialty: This certification is designed for individuals who perform a data science or development role and want to validate their ability to build, train, tune, and deploy machine learning models on AWS.

Experience Requirements for a Data Specialist Role

Typically, a Data Specialist role requires 2-5 years of experience in data analysis, data management, or a related field. Experience with data manipulation, statistical analysis, and machine learning is often necessary. Familiarity with data visualization tools and experience working with large datasets can also be beneficial. For more senior positions, additional experience in project management or team leadership may be required.

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