Salary for Entry-level / Junior Research Analyst during 2023

Salary details

The average entry-level / junior Research Analyst salary lies between USD 50,000 and USD 90,000 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. 24, 2024

Salary trend

Top 20 Job Tags for Entry-level / Junior Research Analyst roles

The three most common job tag items assiciated with entry-level / junior Research Analyst job listings are Research, Python and SQL. 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:

Research | 58 jobs Python | 42 jobs SQL | 30 jobs Statistics | 29 jobs Excel | 26 jobs Economics | 23 jobs Data analysis | 23 jobs R | 17 jobs Market research | 17 jobs Machine Learning | 15 jobs Engineering | 14 jobs Privacy | 13 jobs Consulting | 11 jobs Finance | 11 jobs Power BI | 11 jobs Mathematics | 10 jobs Security | 9 jobs Tableau | 8 jobs Data Analytics | 7 jobs Data visualization | 7 jobs

Top 20 Job Perks/Benefits for Entry-level / Junior Research Analyst roles

The three most common job benefits and perks assiciated with entry-level / junior Research Analyst job listings are Career development, Flex hours and Team events. 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 | 31 jobs Flex hours | 27 jobs Team events | 23 jobs Health care | 15 jobs Startup environment | 15 jobs Competitive pay | 14 jobs Equity / stock options | 12 jobs 401(k) matching | 9 jobs Flex vacation | 9 jobs Insurance | 8 jobs Salary bonus | 8 jobs Medical leave | 7 jobs Parental leave | 5 jobs Wellness | 4 jobs Fertility benefits | 4 jobs Gear | 2 jobs Lunch / meals | 1 jobs Fitness / gym | 1 jobs Conferences | 1 jobs Relocation support | 1 jobs

Salary Composition

The salary for an entry-level or junior research analyst in AI/ML/Data Science typically consists of a fixed base salary, which forms the bulk of the compensation package. This base salary can vary significantly depending on the region, industry, and company size. For instance, tech hubs like San Francisco or New York may offer higher base salaries compared to smaller cities. In addition to the base salary, some companies offer performance-based bonuses, which can range from 5% to 15% of the base salary. Larger companies or those in competitive industries might also provide stock options or equity as part of the compensation package, which can be a significant addition to the overall remuneration.

Steps to Increase Salary

To increase your salary from an entry-level position, consider the following strategies:

• Skill Enhancement: Continuously upgrade your technical skills, especially in high-demand areas like deep learning, natural language processing, or big data analytics.
• Advanced Education: Pursuing a master's degree or specialized certifications can make you more competitive and eligible for higher-paying roles.
• Networking: Build a strong professional network by attending industry conferences, joining relevant online communities, and connecting with peers and mentors.
• Performance Excellence: Consistently exceed performance expectations and take on challenging projects to demonstrate your value to the organization.
• Role Transition: Consider transitioning to roles with more responsibility, such as data scientist or machine learning engineer, which typically offer higher salaries.

Educational Requirements

Most entry-level positions in AI/ML/Data Science require at least a bachelor's degree in a related field such as computer science, data science, mathematics, or statistics. Some employers may prefer candidates with a master's degree, especially for roles that involve more complex data analysis or machine learning model development. A strong foundation in programming languages like Python or R, as well as knowledge of data manipulation and analysis tools, is often essential.

Helpful Certifications

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

• Google Professional Machine Learning Engineer
• Microsoft Certified: Azure AI Engineer Associate
• IBM Data Science Professional Certificate
• AWS Certified Machine Learning – Specialty

These certifications can validate your skills and knowledge, making you a more attractive candidate to potential employers.

Experience Requirements

For entry-level roles, employers typically look for candidates with some practical experience, which can be gained through internships, co-op programs, or relevant projects during your academic studies. Experience with data analysis, machine learning algorithms, and familiarity with tools like TensorFlow, PyTorch, or SQL can be advantageous. Demonstrating your ability to work with real-world datasets and solve practical problems is often more important than the length of your experience.

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