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Quantile reduces risk, notional and capital requirements for market participants trading OTC derivatives.
Our market leading services connect a network of participants and deliver advanced algorithms that rebalance and reduce risk – increasing the efficiency and liquidity of markets, improving returns for clients and making the financial system safer.
Since launching in 2017, Quantile has eliminated hundreds of trillions of dollars of gross notional through interest rate compression and billions of dollars in margin through initial margin optimisation. We recently launched a capital optimisation service to help financial institutions meet new regulatory requirements and reduce their risk-based capital.
Our clients include all of the top tier global banks, regional banks and other large institutional market participants. We are headquartered in London, with offices in New York, Amsterdam and Sydney (Tokyo coming soon!).
About the role
As a graduate in the engineering department, you will collaborate with some of the market’s brightest minds and have the opportunity to make an impact on meaningful projects, including designing and enhancing Quantile’s services and analysing the results.
Quantile’s services can be expressed mathematically as solutions to constrained convex optimisation problems. You will work on converting real world financial data to solve such problems, ultimately delivering clients a set of actionable trades which can be used to optimise their portfolios. Our technology stack is predominantly JavaScript and Python and runs on the public cloud.
The Strats & Modelling team is responsible for designing, building and maintaining the algorithms at the heart of the Quantile services. This typically involves creating a model for a linear of convex optimization problem and interpreting the solution of that problem as a set of financial transactions that should be executed to improve some aspect of a derivatives portfolio. While we use a commercial optimization library for the optimization itself, the scale of the problems that we encounter mean that we are on the leading edge of what today’s software can handle and so we need to have a deep understanding of the behavior of the algorithms.
The successful candidate will work as part of a small team of 5-7 strats to build and research improvements to the Compression engine. They will work directly with the Product Development team to enhance the product, based on feedback from clients and analysis of runs, as well as on strategic projects. Examples of possible projects include:
- Develop new scaling scheme to improve reliability and performance
- Use the solution of our current MIP optimizer as a starting point to a non-linear solver. This would allow greater flexibility in the solutions we propose.
- Investigate how sensitive the solution is to small changes in the data to understand which constraints we should relax for maximum impact
- Introduce new variables in the optimizer to allow variable hedge rates in our proposals
- Improve the runtime performance by investigating and adding heuristics to reduce the data set and solution search space
- Develop new functionality to better validate incoming risk data prior to optimization.
Desired skills
- Solid understanding of python for numerical programs. In particular, familiarity with pandas and numpy
- A strong mathematical background (numerical methods, linear algebra, probability & statistics)
- Understanding of linear programming, mixed integer programming and convex optimization
- Knowledge of Interest Rate Swaps
- Excellent problem-solving skills
This role is a hybrid working role, with a blended approach of home and office working.
Quantile is an Equal Opportunity Employer.
