Elite Quant Research Internship - London Summer Program

G-Research; Greater London; On-site; GBP 113,750 - 159,250; Full time

Benefits offered by this job

Accommodation

Lunch provided

Leave entitlement

Casual dress code

Social activities

Central London office

Job summary

G-Research invites applications for a 10-week Summer Research Programme in London.Participants will tackle a challenging research project that combines mathematical and computational analysis to extract predictive signals from financial time-series.

You will work with a Quantitative Researcher mentor and explore methods including modelling, deep learning and optimisation, with opportunities to back-test and simulate portfolio performance while networking with peers in a world-class research

Qualifications

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

Responsibilities

• Work on a meaningful research project applying mathematical and computational analysis.
• Use techniques from mathematical modelling, deep learning, optimisation and ML.
• Develop and test signals from financial time-series and assess predictability.
• Collaborate with a Quantitative Researcher mentor and receive structured feedback.

Skills

Strong mathematical background

Python or C# programming

Data science / ML interest

Education

Masters or PhD in quantitative subject

PhD preferred

Tools

GitHub

Kaggle

Job description

G-Research invites applications for a 10-week Summer Research Programme in London.Participants will tackle a challenging research project that combines mathematical and computational analysis to extract predictive signals from financial time-series.

You will work with a Quantitative Researcher mentor and explore methods including modelling, deep learning and optimisation, with opportunities to back-test and simulate portfolio performance while networking with peers in a world-class research