A great opportunity to join a strong credit algo quant team for a tier 1 bank.
Credit e-Trading Quant - Assoc/VP level
This Credit Automated Trading Strats team works closely with traders to cover Credit flow products, including Corporate Bonds, Municipal Bonds, Leveraged Loans and Credit Indices.
They apply a scientific approach to trading by combining an understanding of market microstructure with modern data analytics to develop mid/reference price models. The team is also responsible for improving traders' workflow by assisting them with different data analytics. The team also owns the real-time product analytics library/system for various credit flow products.
This opportunity is to join the Credit Automated Trading Strats team in London, with a focus on real time product & data analytics and mid/reference price models for corporate bonds and loans.
- Using data science and statistics to produce data-driven, mid / reference price models;
- Driving design, roll-out and adoption of systems to automate marking and quoting of corporate bonds and loans;
- Raising awareness of the latest cutting-edge tools and technology available to the trading desk;
- Delivery of real time analytics for bonds and loans using a combination of C++ and python;
- Liaise with QR and Technology to execute and roll out improvements to desk processes;
- Research, back-testing and reporting tools for quoting strategies and ongoing improvements to related infrastructure, including leveraging big data technologies.
They work in a very dynamic environment, and excellent communication skills are required in the interaction with trading, technology, and control functions. A healthy interest in good software design principles and testing is essential.
The role requires a detailed understanding of the corporate bond/loan, cds, and index cds markets. It is understood that you may not have this knowledge from previous experience, but you would need to be highly motivated to gain this knowledge. A STEM undergraduate degree is required; a graduate degree is preferred.
- 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-analysis skills on real datasets, gained through hands-on experience;
- Strong OO design skills are required, most likely obtained using C++;
- Extensive Python experience, including familiarity with methods for working with large data and tools for data analysis (pandas, numpy, scikit, Tensor Flow, Spark, etc);
- Reactive programming experience is a plus;
- Attention to detail: thorough and persistent in delivering production quality analytics;
- Excellent problem solving ability;
- Solid communication and presentation skills; can clearly and succinctly summarize analysis for trading and management;
- Ability to work in a high-pressure environment;
- Proactive attitude. Should have a natural interest to learn about our business, models and technology platform.