London Summer Quant Research Internship — Competitive Pay

Cooper & Hall Limited

Greater London

Hybrid

GBP 20,000 - 27,000

Full time

14 days+
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Benefits offered by this job

Accommodation
Weekly intern activities
Lunch provided
30 days annual leave pro-rated
Casual dress code
Central London location

Job summary

G-Research invites applications for a 10-week Summer Research Programme in London. You will tackle a challenging research project applying mathematical and computational analysis to financial time-series. Expect structured feedback, mentorship from a Quant researcher, and a final presentation to senior management.

You will explore a range of techniques from mathematical modelling to deep learning and back-testing frameworks, with a view to potential full-time opportunities after studies.

Qualifications

  • Strong mathematical background.
  • Programming in at least one OO language (Python or C#).
  • Interest in applying data science, ML and optimisation to real problems.
  • Final or penultimate year of a Masters or PhD in a quantitative subject, PhD preferred.
  • GitHub or Kaggle profiles are desirable.

Responsibilities

  • Conduct an independent research project under mentorship.
  • Apply mathematical modelling, ML, and optimisation techniques to financial data.
  • Present findings to senior management at the end of the internship.

Skills

Mathematics
Programming (Python/C#)
Data science & ML
Finance interest
GitHub/Kaggle

Education

Masters/PhD in technical/quantitative field
Strong mathematical background

Tools

Object-oriented programming

Job description

G-Research invites applications for a 10-week Summer Research Programme in London. You will tackle a challenging research project applying mathematical and computational analysis to financial time-series. Expect structured feedback, mentorship from a Quant researcher, and a final presentation to senior management.

You will explore a range of techniques from mathematical modelling to deep learning and back-testing frameworks, with a view to potential full-time opportunities after studies.

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