Salary for Entry-level / Junior Quantitative Analyst during 2024

Salary details

The average entry-level / junior Quantitative Analyst salary lies between USD 70,024 and USD 154,700 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: Dec. 22, 2024

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

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

Python | 87 jobs Statistics | 81 jobs Mathematics | 50 jobs Finance | 42 jobs Engineering | 38 jobs R | 37 jobs Testing | 37 jobs Research | 34 jobs Machine Learning | 29 jobs Trading Strategies | 29 jobs SQL | 29 jobs Economics | 27 jobs Computer Science | 23 jobs ML models | 22 jobs Crypto | 20 jobs Data analysis | 20 jobs Physics | 20 jobs Banking | 16 jobs Matlab | 14 jobs Credit risk | 14 jobs

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

The three most common job benefits and perks assiciated with entry-level / junior Quantitative 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 | 44 jobs Health care | 29 jobs Flex hours | 21 jobs Competitive pay | 20 jobs Equity / stock options | 14 jobs Flex vacation | 14 jobs Startup environment | 13 jobs Insurance | 10 jobs Team events | 8 jobs Salary bonus | 8 jobs Parental leave | 5 jobs Wellness | 5 jobs Transparency | 4 jobs Medical leave | 3 jobs 401(k) matching | 2 jobs Lunch / meals | 1 jobs Conferences | 1 jobs Unlimited paid time off | 1 jobs Paid sabbatical | 1 jobs

Salary Composition

The salary for an entry-level or junior quantitative analyst in AI/ML/Data Science typically comprises a base salary, performance bonuses, and additional remuneration such as stock options or benefits. The base salary is the fixed component and can vary significantly depending on the region, industry, and company size. For instance, tech companies in major hubs like Silicon Valley or New York often offer higher base salaries compared to smaller firms or those in less competitive regions. Bonuses are usually performance-based and can range from 10% to 20% of the base salary, depending on the company's profitability and individual performance. Additional remuneration might include stock options, especially in startups or tech companies, as well as benefits like health insurance, retirement plans, and professional development allowances.

Steps to Increase Salary

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

• Skill Enhancement: Continuously upgrade your skills in AI/ML and data science. Mastering advanced techniques and tools can make you more valuable to your employer.
• Networking: Build a strong professional network. Engaging with industry professionals can open up opportunities for higher-paying roles.
• Performance Excellence: Consistently exceed performance expectations. Demonstrating your ability to deliver results can lead to promotions and salary increases.
• Further Education: Pursue advanced degrees or certifications that are relevant to your field. This can make you eligible for more senior roles.
• Industry Transition: Consider moving to industries that pay higher salaries for quantitative analysts, such as finance or tech.

Educational Requirements

Most entry-level quantitative analyst positions in AI/ML/Data Science require at least a bachelor's degree in a related field such as computer science, mathematics, statistics, or engineering. However, a master's degree or Ph.D. can be advantageous and is often preferred by employers, especially for roles that require more complex data analysis and modeling.

Helpful Certifications

While not always mandatory, certain certifications can enhance your profile and demonstrate your expertise. Some valuable certifications include:

• Certified Analytics Professional (CAP)
• Google Professional Machine Learning Engineer
• Microsoft Certified: Azure AI Engineer Associate
• AWS Certified Machine Learning – Specialty
• Data Science Council of America (DASCA) Certifications

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

Experience Requirements

For an entry-level position, employers typically look for candidates with some practical experience, which can be gained through internships, co-op programs, or relevant projects during your studies. Experience with programming languages like Python or R, familiarity with data analysis tools, and a basic understanding of machine learning algorithms are often expected.

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