Quantitative Research – FX Options – Associate – London

JP Morgan

Location: Greater London

Job Type: Full time


Job Summary:

If you are passionate, curious, and ready to make an impact, we are looking for you.

Quantitative Research (QR) is an expert quantitative modelling group in J.P. Morgan, as well as a leader in financial engineering, data analytics, statistical modelling and portfolio management. As a global team, QR partners with traders, marketers and risk managers across all products and regions, contributes to sales and client interaction, product innovation, valuation and risk management, inventory and portfolio optimization, electronic trading and market making, and appropriate financial risk controls.

We are looking for a talented Quant to join our team in London. The FX Options QR team's mission is to develop and maintain sophisticated mathematical models, cutting-edge methodologies and infrastructure to value and hedge financial transactions ranging from vanilla flow products to complex derivative deals, as well as improve the performance of algorithmic trading strategies and promote advanced electronic solutions to our clients worldwide. We also work closely with trading desks to develop statistical arbitrage strategies and other quantitative trading models.

In addition, we are providing on job training, intensive internal classroom training, and online courses, all given by our experienced quants. Through the diversity of the businesses, it supports and the variety of functions that it is responsible for, Quantitative Research group provides unique growth opportunities for you to develop your abilities and your career.

Job responsibilities:

You’ll contribute to the firm’s product innovation, effective risk management, financial risk controls. Specially, you’ll have the chance to:

  • Develop mathematical models for pricing, hedging and risk measurement of derivatives securities
  • Support both OTC and electronic trading activities by explaining model behavior, identifying major sources of risk in portfolios, carrying out scenario analyses, developing and delivering quantitative tools, and researching for new trading ideas
  • Assess the appropriateness of quantitative models and their limitations, identifying and monitoring the associated model risk
  • Implement risk measurement, valuation models or algorithmic trading modules in software and systems
  • Design efficient numerical algorithms and implementing high performance computing solutions
  • Design and develop software frameworks for analytics and their delivery to systems and applications
  • Write well-formulated documents of model specification and implementation testing

Required qualifications, capabilities, and skills:

  • You have advanced degree (PhD, MSc or equivalent) in Engineering, Mathematics, Physics, Computer Science, etc. Relevant academic research publications a plus;
  • You understand advanced mathematics arising in financial modeling (stochastic calculus, probability theory, partial differential equations, numerical analysis, optimization, statistics);
  • You demonstrate proficiency in code design and programming skills, with primary focus on Python and C++;
  • You’re interested in applying agile development practices in a front-office trading environment;
  • You quickly grasp business concepts outside immediate area of expertise and adapt to rapidly changing business needs;
  • You demonstrate quantitative and problem-solving skills as well as research skills;
  • You’re enthusiastic about knowledge sharing and collaboration; you have a excellent communication skills, both verbal and written, can engage and influence partners and stakeholders.

Preferred qualifications, capabilities, and skills:

  • Knowledge of FX Option payoffs and pricing models
  • Understanding of the different types of financial risk and you can discuss in detail ways of managing these risks;
  • Markets experience and general trading concepts and terminology is useful to be familiar with;
  • Practical data analytics skills on real data sets gained through hands-on experience, including familiarity with methods for working with large data and tools for data analysis (pandas, numpy, TensorFlow).
You’ve got this!