Salary for Entry-level / Junior Quantitative Analyst in United States during 2024

Salary details

The average entry-level / junior Quantitative Analyst salary lies between USD 70,024 and USD 154,700 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

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 consists of a base salary, performance bonuses, and additional remuneration such as stock options or benefits. The base salary is the fixed component and usually forms the largest part of the total compensation package. Performance bonuses can vary significantly depending on the company's profitability and individual performance, often ranging from 10% to 20% of the base salary. Additional remuneration might include stock options, especially in tech companies or startups, and benefits like health insurance, retirement plans, and paid time off. The composition can vary by region, with tech hubs like San Francisco or New York offering higher base salaries but potentially lower relative bonuses due to the high cost of living. Industry also plays a role; for instance, finance and tech sectors might offer more lucrative bonuses compared to academia or government roles. Company size can influence stock options and benefits, with larger companies often providing more comprehensive packages.

Increasing Salary

To increase your salary from an entry-level position, consider pursuing further education or certifications that enhance your expertise and marketability. Gaining experience in high-demand areas such as deep learning, natural language processing, or big data analytics can make you more valuable. Networking within the industry and seeking mentorship can provide insights into career advancement opportunities. Additionally, demonstrating leadership skills and taking on more responsibilities can position you for promotions. Transitioning to a company or region known for higher compensation, or moving into a specialized niche within AI/ML, can also lead to salary increases.

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. is often preferred, especially for roles that involve complex data modeling or algorithm development. Coursework in machine learning, data mining, statistical analysis, and programming languages like Python or R is highly beneficial. Some positions may also require knowledge of specific tools or platforms, such as TensorFlow or Hadoop.

Helpful Certifications

Certifications can enhance your credentials and demonstrate expertise in specific areas. Some valuable certifications include:

• Certified Analytics Professional (CAP): Validates your ability to transform data into valuable insights.
• Google Professional Machine Learning Engineer: Demonstrates proficiency in designing, building, and productionizing ML models.
• AWS Certified Machine Learning – Specialty: Focuses on building, training, and deploying ML models on the AWS platform.
• Microsoft Certified: Azure AI Engineer Associate: Covers AI solutions on the Azure platform.

These certifications can help differentiate you from other candidates and may lead to better job opportunities and higher salaries.

Experience Requirements

For entry-level positions, 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 data analysis, statistical modeling, and machine learning algorithms is often required. Familiarity with programming languages such as Python, R, or SQL, and experience with data visualization tools like Tableau or Power BI, are also commonly expected. While direct work experience in a professional setting is ideal, demonstrating your skills through personal projects or contributions to open-source projects can also be valuable.

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