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$56.5K

$133.9K

$240K

How much do quantitative analyst internships jobs pay per year?

As of Aug 22, 2026, the average yearly pay for quantitative analyst internships in the United States is $133,877.00, according to ZipRecruiter salary data. Most workers in this role earn between $111,500.00 and $145,500.00 per year, depending on experience, location, and employer.

What is a quantitative analyst internship?

Quantitative Analyst Internships are short-term positions, typically offered to students or recent graduates, that provide hands-on experience in applying mathematical, statistical, and programming skills to solve financial or business problems. Interns work under the guidance of experienced quantitative analysts, often known as 'quants', and assist in tasks such as data analysis, model development, and risk management. These internships are common in industries like finance, investment banking, and technology, and can serve as a stepping stone to full-time roles after graduation. Interns gain exposure to real-world quantitative methods, learn industry-standard tools and software, and develop critical problem-solving skills. Completing a quantitative analyst internship can significantly enhance a candidate’s employability in quantitative and analytical careers.

What types of projects do quantitative analyst interns typically work on during their internship?

Quantitative Analyst Interns often work on projects involving data analysis, financial modeling, and algorithm development to support trading or risk management teams. These projects may include backtesting trading strategies, optimizing portfolio allocations, or analyzing large datasets to uncover market trends. Interns usually collaborate closely with experienced quantitative analysts, software engineers, and traders, gaining exposure to real-time problem-solving and decision-making. This hands-on experience not only strengthens analytical and programming skills but also provides valuable insights into the workflow of a professional quant team.

What are the key skills and qualifications needed to thrive as a quantitative analyst intern, and why are they important?

To thrive as a Quantitative Analyst Intern, you need strong quantitative and analytical skills, proficiency in statistics, and a background in mathematics, finance, or a related field. Familiarity with programming languages like Python, R, or MATLAB, and experience with data analysis tools and financial modeling software are highly valued. Attention to detail, problem-solving ability, and effective communication distinguish top candidates in this role. These skills are crucial for analyzing complex data sets, developing models, and communicating insights that drive informed financial decisions.
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Quantitative Analyst Internships jobs near you
Infographic showing various Quantitative Analyst Internships job openings in the United States as of August 2026, with employment types broken down into 89% Full Time, 5% Part Time, and 6% Contract. Highlights an 80% Physical, 9% Hybrid, and 11% Remote job distribution, with an average salary of $133,877 per year, or $64.4 per hour.

Quantitative Research Analyst - Jersey City, NJ

Quant Blueprint LLC

Manhattan, NY • On-site

$136.50 - $204.75/hr

Other

Posted 4 days ago

Job description

Overview

Vatic Labs is a quantitative trading firm in New York. Our traders, AI researchers, and technologists collaborate to develop autonomous trading agents and cutting edge technology.

Role

As an Quantitative Research Intern at Vatic Labs, you will contribute to the research and development of fully autonomous trading agents with some of the brightest researchers, traders, and technologists in the world. As a part of your internship at Vatic Labs, you will explore vast amounts of market data, research different AI approaches, apply cutting edge machine learning algorithms and statistical approaches to this data to discover and capitalize on trading opportunities.

Approach and Environment

We are seeking researchers who have demonstrated the ability to generate impactful research in their academic pursuits. We foster an open and academic environment, where collaboration is the key to our success. Drawing from our collective backgrounds in Computer Science, Mathematics, Statistics, and Physics, we apply rigorous analytics to test hypotheses derived from years of successful quantitative trading. We are passionate about hiring the best and the brightest, empowering them with the tools and mentorship needed to be successful.

Responsibilities and Qualifications
  • Earned or will earn a PhD or Master\'s Degree in Computer Science, Statistics, Mathematics, Electrical Engineering, Physics, or related fields
  • Demonstrable experience coding in C++ or Python in a Linux environment
  • Have experience analyzing large datasets with rigorous statistical and machine learning approaches, including classification, clustering, and regression.
  • In depth technical knowledge of AI, deep learning, and machine learning algorithms including strong knowledge of the mathematical underpinnings behind these various methods
  • Have innate curiosity for understanding why and how certain techniques work
  • Have deep knowledge of time-series analysis
  • Advanced knowledge of a high-level language for numerical analysis, Python (numpy/scipy stack) preferred
  • Contribution to AI and ML research communities, top tiered peer reviewed publications, publishing/presenting papers at conferences such as NIPS, ICML, etc.
  • Have an interest and enthusiasm for learning about financial markets (previous experience not required)
Perks and Culture
  • While we are serious about our work at Vatic, we also promote a fun environment!
  • Ping pong and poker games
  • Fun team outings
  • Unlimited office snacks
  • Free breakfast, lunch, and dinner
Compensation

The base salary range for this role is between $3000 and $4500 weekly. The base salary range does not include any other form of compensation, such as any bonus amounts, or any benefits. Factors that may impact the agreed upon base salary within the range for a particular candidate include years of experience, level of education obtained, skill set, and other factors.

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