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Machine Learning Systematic Equities Quantitative Researcher

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Job Description - Machine Learning Systematic Equities Quantitative Researcher

£Competitive Base Salary + Market Leading Bonus GBP

Onsite WORKING

Location: Central London, Greater London - United Kingdom Type: Permanent

TEAM OVERVIEW

Our client is a globally leading quant hedge fund, and a quantitative research and trading team with a strong track record in systematic equities. The founding members have approximately 70 years quantitative trading experience between them, with backgrounds from the senior ranks at top tier institutions. Having established the core infrastructure, trading processes and delivered performance, they are now looking to aggressively expand across multiple areas of the business. For the right individual they offer a highly rewarding front office role in a fast paced and collaborative environment, where each individual's impact can be clearly seen.

PRINCIPAL RESPONSIBILITIES
  • Work alongside the Portfolio Manager on developing systematic trading strategies
  • Primary focus on idea generation, data gathering and research/analysis, model implementation, and backtesting
  • Work on state of the art machine learning techniques to extract alphas for statistical arbitrage strategies
REQUIRED TECHNICAL SKILLS
  • Demonstrable experience in the latest ML techniques in a production setting
  • Strong programming skills in any object-oriented language such as Python and C++
  • Bachelors, Masters, or PhD in a quantitative subject such as Computer Science, Applied Mathematics, Statistics, or related field from a top ranked university
PREFERRED EXPERIENCE
  • 2-5 years of experience working in a quantitative research/trading capacity with a focus on mid-to-high frequency equities and/or futures strategies
  • Experience with signals that use non-linear machine learning models, such as SVMs, GBMs, or DNNs.
  • Hands on experience with PyTorch, TensorFlow, or similar packages.
HIGHLY VALUED RELEVANT EXPERIENCE
  • Prior research in applying machine learning models on intraday securities return prediction.
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