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Internship Mathematical Technology Jobs in Edison, NJ

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Internship Mathematical Technology information

What is the difference between Internship Mathematical Technology vs Data Analyst?

AspectInternship Mathematical TechnologyData Analyst
Required CredentialsTypically pursuing or recent graduate in mathematics, computer science, or related fieldsBachelor's degree in statistics, mathematics, or related field; often requires some experience
Work EnvironmentInternship setting, often in tech or finance companies, focusing on learning and support rolesFull-time role in various industries, analyzing data to inform business decisions
Employer & Industry UsageUsed in tech, finance, research institutions for technical support and project assistanceCommon in marketing, finance, healthcare, and tech sectors for data-driven decision making

Internship Mathematical Technology focuses on gaining practical experience in mathematical tools and programming, often as a learning role. Data Analysts perform ongoing data interpretation and reporting, requiring more experience and responsibility. Both roles involve working with data and mathematical concepts but differ in scope, experience level, and industry application.

Internship Mathematical Technology jobs near you
Infographic showing various Internship Mathematical Technology job openings in Edison, NJ as of July 2026, with employment types broken down into 73% Full Time, 19% Part Time, and 8% Contract. Highlights an 78% Physical, 3% Hybrid, and 19% Remote job distribution.

Summer 2027 Quantitative Research Internship

Point72

New York, NY

$240K - $300K/yr

Full-time, Temporary, Internship

Re-posted 29 days ago

Job description

Please send CVs to KEPL-talent@cubistsystematic.com with "2027 QR Summer Internship Application" in the subject line. When your application is received, we will consider you for all similar positions at Cubist.

About Our Firm:

Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources.

About Our Team:

KEPL is a fast-growing team at Cubist Systematic Strategies. We specialize in trading medium-frequency statistical arbitrage strategies with high Sharpe. The team is made up of people from top universities and top tier trading and tech firms. We have an open and collaborative culture, and we value rigorous research and innovative technologies.

Role / Experience:

We are looking for exceptional students to be our quantitative researcher interns for the summer of 2027. An ideal candidate should have a strong passion and initiative to work in a start-up environment. He/she should have strong analytical skills and be able to solve hard problems rigorously. Our typical intern candidates come from quantitative PhD programs of top US universities.

Our internship program offers unique KEPL experience. During the internship, our interns will receive rigorous and comprehensive trainings. They will develop strong research skills through working closely with our full-time researchers on brand new quant trading models with real-world impact. We will consider full-time offers for interns after the internship.

Requirements:

  • PhD candidate in math/physics/statistics/EE/CS, or other quantitative fields
  • Strong knowledge of computational math, probability, and statistics
  • Strong analytical skills, with attention to details
  • Good communication skills
  • Willing to work in a fast-paced start-up environment
  • Willing to learn and to take ownership
  • Strong programming skills in Python or C/C++
  • Commitment to the highest ethical standards

The annual base salary is $240,000-$300,000 (USD) which will be prorated based on internship start and end date. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.

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