Quant Research Intern: ML-Driven Trading Ideas

Quant Research Intern: ML-Driven Trading Ideas

Full-Time 22500 - 27500 £ / year (est.) No working from home possible
Trading Interview

At a Glance

  • Tasks: Dive into quantitative research, tackling trading challenges with ML and statistical methods.
  • Company: Join DRW, a leading firm in innovative trading solutions.
  • Benefits: Gain hands-on experience, mentorship, and networking opportunities in finance.
  • Other info: Collaborative environment with exposure to diverse trading strategies.
  • Why this job: Make an impact in the fast-paced world of trading while learning from industry experts.
  • Qualifications: Strong analytical skills and a passion for finance and technology.

The predicted salary is between 22500 - 27500 £ per year.

DRW is offering a Quantitative Research Intern position in London, working on problems from a trading environment using statistical methods, ML techniques and derivatives pricing concepts.

You will access the team’s research infrastructure for simulation, back-testing and validation of models.

The role emphasizes collaboration with traders and researchers, with exposure to multi-asset strategies across geographies.

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Quant Research Intern: ML-Driven Trading Ideas employer: Trading Interview

Tower Research Capital is an exceptional employer that fosters a dynamic and collaborative work culture, where innovation and rigorous experimentation are at the forefront. Located in a vibrant financial hub, employees benefit from cutting-edge technology and resources, alongside ample opportunities for professional growth and development within the fast-paced world of quantitative trading. Join us to be part of a team that values your contributions and rewards your success in a meaningful way.

Trading Interview

Contact Details:

Trading Interview Recruitment Team

We think you need these skills to ace Quant Research Intern: ML-Driven Trading Ideas

Statistical Methods
Machine Learning Techniques
Derivatives Pricing Concepts
Simulation
Back-Testing
Model Validation
Collaboration