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Seven Research Spring Camp - Graduate Student

New York, NYOnsiteFull TimeJunior / Entry-levelPosted 15 days ago

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Required skills

  • STEM students
  • computational fields
  • applied mathematics
  • statistics
  • physics
  • quantitative background
  • programming background
  • analytical challenges
  • real-world challenges
  • resume

Nice to have

  • quantitative research
  • advanced technology
  • modern financial systems
  • mathematical concepts
  • financial concepts
  • electronic trading systems

Role summary

Seven Research is hosting a two-day Spring Camp in NYC for exceptional STEM Master's and PhD students graduating in 2028 or later. The program focuses on quantitative research, advanced technology, and financial systems, including mathematical and financial concepts, electronic trading systems, and research/testing/validation processes. Participants will engage in tech talks on quantitative concepts and machine learning, collaborative challenges mirroring real research problems in quantitative finance, and conversations with team members about research methodologies, AI, and career paths. This immersive experience aims to provide insights into quantitative research and interview preparation.

Seven Research Spring Camp – March 13-14, SoHo, NYC

Seven Research invites exceptional STEM students to our New York office for an immersive exploration of quantitative research, advanced technology, and modern financial systems. Over the course of the program, participants will learn about mathematical and financial concepts central to quantitative research, the fundamentals of electronic trading systems, and the research, testing, and validation processes that power our unique, systematic approaches. The experience includes interactive technical sessions, hands-on problem-solving activities, and 1:1 conversations with leaders across our teams.

Program Details

  • Program Dates: March 13-14, 2027
  • Location: Seven Research, SoHo, New York City
  • Travel, meals, and lodging: Fully covered
  • Arrival/Departure: Arrive by the evening of March 13 and depart by the morning of March 15. The main sessions are on March 14 with optional activities on the evening of March 13.

    • What to Expect

  • Tech Talks on quantitative concepts, machine learning applications, and demonstrations of how research and engineering teams design, test, and iterate on ideas
  • Collaborative Challenges mirroring real research challenges that address topical problems in quantitative finance
  • Conversations with Team Members about research methodologies, problem-solving strategies, AI-driven tooling, careers paths in quantitative research, and interview preparation
  • Community-Building Events including a team dinner, games, and dynamic group activities

    • Who Should Apply

  • Full-time Master’s and PhD students in computationally rigorous fields such as computer science, applied mathematics, statistics, and physics
  • Graduating in 2028 or later
  • Strong quantitative and/or programming background
  • Interest in applying technology to analytical, real-world challenges

    • Application Details

  • Deadline: December 17, 2026 at 11:59 PM ET (applications reviewed on a rolling basis)
  • Materials: Resume, transcript, and a written response (up to 300 words)
  • Next Steps: Selected applicants will take part in a brief, non-technical call with our Recruiting team
  • Decisions: All invitations will be sent by January 31, 2027

    • Applicant Privacy

    Please review our Applicant Privacy Notice for information about how we handle candidate information.

    Accommodations

    If you need a reasonable accommodation to participate in any part of our application or interview process, please contact careers@sevenresearch.com. We’ll work with you to support your participation.

    We are an equal opportunity employer and prohibit discrimination and harassment without regard to race, religion, age, sex, or any other characteristic protected by law.

    Ready to apply?
    You'll be redirected to Seven Research's application page.

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