Vacancy detail

Entry Level Quantitative Research Position – Cross Asset

Location: United Kingdom (London, City of) Type: Permanent Start date: 14/09/2017

Entry Level Quantitative Research Position – Cross Asset

 

Job Summary:
My client has the need for an entry level graduate who is looking to take their first steps into industry within a quantitative environment. This is a great opportunity to have the freedom to focus on a particular asset derivative or trading desk.  The role offers the freedom to learn a particular business area along with its product and models, while contributing to the model development and model support effort for that area.

 

Core Responsibilities:

  • Develop models and implement them in software for pricing and risk managing derivatives.
  • Develop pricing and calibration tools.
  • Benchmark and compare results of various techniques
  • Implement products using pricing engines and models
  • Explain model behaviour and predictions to traders, identify major sources of risk in portfolios, carry out scenario analyses, provide guidance / debug analytics
  • Rapid prototyping of models and products

 

Essential skills, experience, and qualifications

  • PhD or equivalent degree from top tier schools/programs in Math, Math Finance, Physics, or Engineering
  • Excellence in probability theory, stochastic processes, partial differential equations, and numerical analysis
  • Very strong analytical and problem solving abilities
  • C/C++ coding with emphasis on numerical methods
  • Good communication skills.

 

Desirable skills/experience:

·         Deep understanding of options pricing theory (i.e. quantitative models for pricing and hedging derivatives) an advantage

  • Strong interest in trading and extracurricular involvement in societies
  • Market knowledge

 

 

Additional information:

This candidate will preferably have relevant quantitative research experience.

 

If this role matches your experience, please apply, or feel free to get in touch with myself at arif.khan@ansonmccade.com

Reference: AMC/AK654. To apply for this vacancy: email Arif Khan

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