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G-Research

Quantitative finance research applying rigorous methods

Quantitative Research Intern

Quantitative ResearchConfirmed live in the last 24 hours
Internship · Summer 2027London, UKIn-person

About the Job

Requirements

  • A strong mathematical background is required.
  • Intermediate-level programming experience in at least one object-oriented language is required, ideally Python or C#.
  • An interest in applying data science, machine learning, and optimization techniques to real-world problems is required.
  • Applicants must be in the final or penultimate year of a Master's or PhD in a highly technical or quantitative subject such as Mathematics, Physics, Statistics, Engineering, or Computer Science.
  • An interest in finance and a willingness to learn are essential.

Responsibilities

  • Investigate foundational questions, uncover market insights, and conduct quantitative research.
  • Apply innovative mathematical and computational analysis to a meaningful research project.
  • Use rigorous scientific methodology, robust statistical analysis, and pattern recognition to extract predictive signals from financial time series and predict future dynamics.
  • Apply mathematical modeling, deep learning, optimization, and machine learning techniques.
  • Implement back-testing frameworks to assess signal robustness when required.
  • Create pipelines that construct and simulate the performance of portfolios derived from input signals when required.
  • Collaborate with a Quantitative Researcher while completing an independent project.
  • Use structured feedback and reviews to improve and develop throughout the internship.
  • Present research ideas to senior management.

Desired Qualifications

  • An active GitHub or Kaggle profile is desirable.
  • A PhD is preferred.

About the Company

G-Research

Quantitative finance research applying rigorous methods

G-Research applies scientific rigour to solve complex challenges in financial markets. It brings together world-class researchers and engineers to develop data-driven models and trading ideas, testing them through rigorous experimentation and robust infrastructure. Unlike many traditional asset managers, its culture emphasizes deep exploration, reproducibility, methodical execution, and long-term thinking. The goal is to generate alpha through disciplined, evidence-based research that informs investment decisions and advances understanding of financial markets.

Company size
1,001-5,000
Company stage
Not listed
Total funding
Not listed
Headquarters
London, United Kingdom
Founded
2001

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