Salary for Mid-level / Intermediate Encounter Data Management Professional during 2024

Salary details

The average mid-level / intermediate Encounter Data Management Professional salary lies between USD 47,700 and USD 65,600 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 Mid-level / Intermediate Encounter Data Management Professional roles

The three most common job tag items assiciated with mid-level / intermediate Encounter Data Management Professional job listings are SQL, Excel and Data management. 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 | 11 jobs Excel | 11 jobs Data management | 11 jobs SAS | 7 jobs Research | 6 jobs Data analysis | 5 jobs Security | 4 jobs Oracle | 2 jobs Finance | 1 jobs Power BI | 1 jobs Testing | 1 jobs Databricks | 1 jobs

Top 20 Job Perks/Benefits for Mid-level / Intermediate Encounter Data Management Professional roles

The three most common job benefits and perks assiciated with mid-level / intermediate Encounter Data Management Professional job listings are Insurance, Parental leave 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:

Insurance | 11 jobs Parental leave | 10 jobs Health care | 10 jobs Competitive pay | 10 jobs Medical leave | 10 jobs Salary bonus | 6 jobs Flex hours | 5 jobs Wellness | 3 jobs Career development | 3 jobs 401(k) matching | 1 jobs Flex vacation | 1 jobs Startup environment | 1 jobs Team events | 1 jobs

Salary Composition

The salary for a Mid-level/Intermediate Encounter Data Management Professional in AI/ML/Data Science typically comprises a fixed base salary, performance bonuses, and additional remuneration such as stock options or benefits. The fixed base salary is the most significant component, often accounting for 70-85% of the total compensation package. Performance bonuses can vary widely, ranging from 5-20% of the base salary, depending on the company's performance and individual achievements. Additional remuneration, such as stock options, profit-sharing, or comprehensive benefits packages, can make up the remaining 5-15%.

Regional differences play a significant role in salary composition. For instance, professionals in tech hubs like Silicon Valley or New York may receive higher base salaries and more substantial stock options compared to those in smaller markets. Industry also influences compensation; tech companies might offer more competitive packages than healthcare or finance sectors. Company size can affect salary composition as well, with larger companies often providing more comprehensive benefits and bonuses.

Steps to Increase Salary

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

• Skill Enhancement: Continuously update and expand your skill set, particularly in emerging AI/ML technologies and tools. Specializing in niche areas can make you more valuable.
• Advanced Education: Pursuing further education, such as a master's degree or specialized certifications, can position you for higher-paying roles.
• Networking: Build a strong professional network to learn about new opportunities and gain insights into industry trends.
• Performance Excellence: Consistently exceed performance expectations and take on challenging projects to demonstrate your value to the organization.
• Negotiation: When the opportunity arises, negotiate for higher pay by leveraging your achievements and market research.

Educational Requirements

Most mid-level positions in AI/ML/Data Science require at least a bachelor's degree in a relevant field such as computer science, data science, statistics, or engineering. A strong foundation in mathematics and programming is essential. Some employers may prefer candidates with a master's degree, especially for roles that involve complex data analysis or machine learning model development.

Helpful Certifications

While not always mandatory, certain certifications can enhance your credentials and demonstrate your expertise:

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

These certifications can validate your skills and make you more competitive in the job market.

Experience Requirements

Typically, a mid-level position requires 3-5 years of relevant experience in data management, data analysis, or a related field. Experience with data modeling, data warehousing, and proficiency in programming languages such as Python or R is often expected. Familiarity with machine learning frameworks and tools like TensorFlow, PyTorch, or scikit-learn is also advantageous.

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