An exciting FinTech startup brings a proven, unique blend of business, quantitative and technology skills to the industry to provide post-trade risk reduction services to major derivative market participants. The firm is expanding rapidly and already counts the top 20 investment banks as paying customers. The UK based firm is opening a New York office in September.
Our focus is deploying sophisticated technology to analyse banks trade and risk data in the cleared and non-cleared markets, generate multilateral risk-mitigating transaction proposals and then organize the execution of such proposals. The resulting risk reduction leads to commensurate benefits in transaction costs, improved market liquidity, lower capital and margin costs and improved returns for banks and their clients.
The firm is a very progressive and rapidly expanding company. We pride ourselves on being a nimble organisation that can respond rapidly to client demands and needs through leveraging sophisticated technology and our skilled workforce. We want to attract dynamic, innovative and curious individuals who are passionate about being the best they can be. We strive to uphold the highest standards of integrity within our company. Our ambition is to grow our people organically and to create a positive, friendly culture.
- Degree in numerate discipline : maths, physics, computer science, engineering
- Good problem solver : is capable of tracking down and resolving complex issues such as :
- after running for 1 hour, a complex algorithm returned a result that is lower than expected . Why ?
- after running for 1 hour, a complex algorithm halts with unable to find result
- experience with python (especially numpy and pandas)
- experience with linux command line (bash) and python REPL
- Basic understanding of fixed income derivatives
- What is an interest rate swap ?
- What greeks should a swap have. What greeks should a swaption have ?
- What is put / call parity
- What is a yield curve / vol curve / vol surface
- Nice to have :
- Specific mathematical knowledge:
- Linear programming (Simplex, Interior Point)
- Linear algebra (Cholesky decomposition, Singular Value Decomposition)
- Convex optimisation
- Mixed integer optimisation
- Computer Science
- Graphs (eg dask)
- Functional programming
- Performance tuning (valgrind)
- Business :
- Yield curve construction
- Model calibration
- Swap pricing
- Vanilla option pricing and risk
- Propose and implement enhancements to the optimisation algorithm (ampl, gurobi, c++, python) and infrastructure (mostly python)
- Identify and fix bugs
- Work with IT development team &Quant Modelling team to
- Create continual improvements to the system
- Create unit and system tests to verify the functional and non functional correctness of the system
- Generate proposals of trade events (new trades, terminations, amendments) to create optimised derivative portfolios for internal and external clients according to provided schedules that satisfy externally spcecified constraints
- Live runs follow a prescribed and inflexible schedule with fixed timeframes (generally over a period of hours). There are 3-5 Live runs per week
- Typically each client may request between 1 and 10 additional scenarios per live run
- internal business unit may request another 10-50 scenarios per run
- overall it may be required to run ~100 scenarios per live run
- Business Units request additional non live runs to for current and prospective clients to demonstrate
- Generate risk reports for internal and external clients
- Analyse bank supplied risk and trade data to identify
- Bad valuations
- Opportunities for compression and optimisation