Job summary:
The position is within the (Equities) Market Risk Quantitative Research team, which sits within the wider Quantitative Research team. The team is responsible for the quantitative framework of the risk management (in particular VaR & Stress) and regulatory capital of the Global Equities business. This includes responsibility for methodology, model development, testing and implementation of analytics used to monitor and analyse the performance of the framework. It works also closely with other asset class Market Risk Quantitative Research teams, other Quantitative Research team members (in particular the Equities valuation team), coverage teams (Trading and Market Risk), Technology and Model Risk teams.
As part of the firm’s effort to enhance the strategic risk system there is a strong requirement for new model development, involving significant research and development, and implementation in python. We are looking for a quantitative analyst/associate for a versatile role which mixes derivatives quant skills with statistical (& machine learning) modelling. Derivatives pricing knowledge, strong coding skills and good communication, being able to work well both independently and as part of team are critical for this role.
Core Responsibilities:
- Carry out research projects in order to define methodologies and improve Market Risk and regulatory capital framework
- Implementation of new models and maintenance of the existing code base
- Provide a quantitative support to the users (trading, market risks coverage teams)
- Liaise with various functions such as Market Risk Technology, Market Risk Coverage, Market Risk Core Analytics, Quantitative Research and Model Risk
Essential skills, experience, and qualifications:
- PhD/Masters or equivalent degree in Maths, Physics, Math Finance, Data Science or Engineering
- Python or C/C++ programming experience, preferably in production environment
- Strong analytical and problem solving abilities
- Excellent communication skills, both verbal and written
- Good understanding of derivatives valuation models
- Expertise in Probability, Statistics or Machine learning and Data Visualization
Desirable skills, experience, and qualifications:
- Knowledge of quantitative risk modelling; VaR, risk capital models, stress methodology
- Coded in production environment with knowledge of software life-cycle
- Model development experience
- Equity derivatives product and pricing knowledge
- Derivative pricing, probability theory, stochastic process, partial differential equations, numerical analysis
