9 дней назад
PhD Winter Internship - 2027 (Quantitative Research)
5 800$
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
PhD Winter Internship - 2027 (Quantitative Research): A three-week in-person introduction to quantitative research through independent projects, trading education, and model training on a high-performance compute cluster with an accent on algorithmic trading, machine learning, and technical research. Focus on training models, solving research problems, and developing programming and quantitative reasoning skills in a New York City office.
Location: In person at the New York City office in New York, United States
Weekly base salary: $5,800, plus company-paid housing, meals, and other perks.
Company
applies scientific methods and sophisticated computing systems to algorithmic trading and financial research.
What you will do
- Learn about a range of trading styles from experienced practitioners.
- Complete independent research projects representative of quantitative research work.
- Train models on a state-of-the-art compute cluster.
- Attend technical talks and participate in social events in New York City.
Requirements
- Full-time PhD student in a technical or quantitative discipline, graduating between fall 2027 and summer 2029.
- Relevant fields include mathematics, physics, computer science, statistics, machine learning, or a related program.
- Demonstrated programming experience; familiarity with Python and/or C++ is preferred.
- Strong problem-solving and communication skills, with enthusiasm for learning new topics.
- The program is intended for candidates with little or no quantitative trading experience.
Culture & Benefits
- Three-week program beginning January 4, 2027.
- Company-paid housing and meals.
- Access to technical talks and a research-focused computing environment.
- Open and collaborative culture focused on innovation, transparency, and diverse expertise.
Hiring process
- Applications must be submitted by October 16, 2026.
- AI tools are prohibited during interviews or assessments unless explicitly authorized.
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