Salary for Senior-level / Expert Encounter Data Management Professional during 2024

Salary details

The average senior-level / expert Encounter Data Management Professional salary lies between USD 69,800 and USD 96,200 globally. 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.

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

Top 20 Job Tags for Senior-level / Expert Encounter Data Management Professional roles

The three most common job tag items assiciated with senior-level / expert Encounter Data Management Professional job listings are Data management, Excel and SQL. 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:

Data management | 6 jobs Excel | 5 jobs SQL | 2 jobs Finance | 2 jobs Data analysis | 2 jobs Python | 1 jobs Research | 1 jobs Security | 1 jobs Power BI | 1 jobs Databricks | 1 jobs Bigtable | 1 jobs

Top 20 Job Perks/Benefits for Senior-level / Expert Encounter Data Management Professional roles

The three most common job benefits and perks assiciated with senior-level / expert Encounter Data Management Professional job listings are Parental leave, Wellness and Health care. 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:

Parental leave | 6 jobs Wellness | 6 jobs Health care | 6 jobs Competitive pay | 6 jobs Medical leave | 6 jobs Insurance | 6 jobs Salary bonus | 6 jobs 401(k) matching | 1 jobs Career development | 1 jobs Gear | 1 jobs Startup environment | 1 jobs Team events | 1 jobs

Salary Composition

The salary composition for a Senior-level/Expert Encounter Data Management Professional in AI/ML/Data Science can vary significantly based on region, industry, and company size. Typically, the salary is divided into a fixed base salary, performance bonuses, and additional remuneration such as stock options or profit-sharing. In regions with a high cost of living, such as major tech hubs in the United States, the base salary might be higher to compensate for living expenses. In contrast, companies in smaller markets might offer lower base salaries but compensate with more substantial bonuses or stock options. Industry also plays a role; for instance, tech companies might offer more in stock options, while healthcare or finance might provide higher bonuses. Larger companies often have more structured compensation packages, including comprehensive benefits, while startups might offer equity as a significant part of the remuneration.

Increasing Salary

To increase your salary from this position, consider the following strategies:

• Skill Enhancement: Continuously update your skills in the latest AI/ML technologies and tools. Specializing in a niche area can make you more valuable.
• Advanced Education: Pursuing further education, such as a master's or Ph.D., can open doors to higher-paying roles.
• Networking: Building a strong professional network can lead to opportunities in higher-paying companies or roles.
• Leadership Roles: Transitioning into managerial or leadership positions can significantly increase your earning potential.
• Industry Change: Moving to a higher-paying industry, such as finance or tech, can also result in a salary increase.

Educational Requirements

Most senior-level positions in AI/ML/Data Science require at least a bachelor's degree in a related field such as computer science, data science, statistics, or engineering. However, a master's degree or Ph.D. is often preferred, especially for expert-level roles. These advanced degrees provide a deeper understanding of complex algorithms and data management techniques, which are crucial for high-level positions.

Helpful Certifications

Certifications can enhance your credentials and demonstrate your expertise to potential employers. Some valuable certifications include:

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

These certifications can validate your skills and knowledge, making you a more competitive candidate for senior roles.

Required Experience

Typically, a senior-level position requires at least 5-10 years of experience in data management, AI, or ML. This experience should include hands-on work with data analysis, machine learning models, and data infrastructure. Experience in leading projects or teams is also highly valued, as it demonstrates your ability to manage complex tasks and collaborate effectively.

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