Quantitative Research - Credit eTrading Strat - Associate

Last updated an hour ago
Location:Greater London
Job Type:Full Time


This exciting opportunity is at the intersection of trading, data science and quantitative research. The candidate would join our Credit Systematic Market Making team in London as an Associate, working as part of the Credit Trading desk to shape the future of the bond portfolio trading business. This is a unique opportunity to combine state-of-the-art machine learning skills with systematic trading and risk management.

Our team

The Credit Systematic Market Making team is part of the EMEA Credit Trading and performs end-to-end systematic trading of credit products, including Corporate and Emerging Market Bonds. We apply a scientific approach to trading by combining an understanding of market microstructure with modern data analytics to develop quoting, market-making and hedging strategies.

The candidate will be part of Quantitative Research, which is responsible for providing quantitative analytics and models to facilitate and optimize trading and risk management in the global CIB Markets Business. The responsibilities of the team span the full range from new model specification, research and implementation, applying for model approval, to integration into production systems as well as their day-to-day support.

Key responsibilities

  • Development, deployment and support of algorithms and tools for real-time pricing of corporate bonds;
  • Applying machine learning and statistical techniques to EMEA bond markets;
  • Conduct research and statistical analysis to build and refine monetization systems for trading signals, including alpha signals, hedging strategies and liquidation strategies;
  • Written and verbal communication with Model Review Groups in order to make models pass strict in-house standards.


We work in a very dynamic environment. Excellent communication skills are required in our interaction with trading, technology, and control functions. The role requires a commercial mindset. A healthy interest in good software design principles is important. Prior experience working in a data driven research environment is an absolute requirement; a Ph.D. (or equivalent) in a machine learning subject or related quantitative field from a top academic institution is a plus.

Essential skills

  • Familiarity with fixed income analytics such as curve building and spread analytics;
  • Strong data science background, including statistics, probability and machine learning; familiarity with concepts as parameter optimization, regularization, neural networks or Gaussian processes;
  • Excellent practical data analytics skills on real data sets, including familiarity with methods for working with large data and tools for data analysis (pandas, numpy, scikit, TensorFlow);
  • Good programming and OO design skills. Java and Python will be a plus, as would experience with reactive programming and a strong understanding of scripting languages;
  • Good understanding of numerical algorithms and data structures;
  • Attention to detail: thorough and persistent in delivering production quality analytics;
  • Ability to work in a high-pressure environment;
  • Pro-active attitude: a self-learner, the candidate should be passionate about problem solving and should have a natural interest to learn about our business, models and infrastructure.