Vacancy detail

Systematic Equities Quant Researcher

£100,000 – £200,000 performance based bonus

Location: United Kingdom (London – London) Type: Permanent

Systematic Equities Quant Researcher


Our client is a top tier global hedge fund with a strong commitment to leveraging market innovations in technology and data to deliver high-quality returns.
This is a Quantitative Researcher role with a focus on intraday or mid-frequency equities as part of a thriving, dynamic, collaborative investment team.

Principal Responsibilities

  • Conduct alpha research and strategy development with a primary focus on: idea generation, data gathering and research/analysis, model implementation and back testing for systematic global equities strategies with intraday or medium-frequency holding periods
  • Combine sound financial insights and statistical learning techniques to explore, analyze, and harness a large variety of datasets in order to build strong predictive models which will be deployed to the investment process
  • Collaborate with the SPM and other team members in a transparent environment, specifically collaborating across books and engaging with the whole investment process (portfolio construction, risk management, etc.)

Preferred Technical Skillset

  • Strong research and programming skills
  • Bachelors, Masters or PhD degree in a quantitative subject such as Applied Mathematics, Statistics, Computer Science or related field from a top ranked university
  • Strong C++ or Python coding capabilities
  • Demonstrate strong abstract reasoning and independent problem-solving skills

Preferred Experience

  • 3-5 years of experience working in a quantitative research capacity focusing on systematic equities
  • Experience developing and deploying strategies in the global equities space

Highly Valued Relevant Experience

  • Experience exploring, researching, and deploying trading signals from various sources of data
  • Experience in quantitative finance, econometrics, and asset pricing
  • Curious, ambitious, self-starter mindset

Reference: SEQR123

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