Quant Developer

Cititec Talent Limited • London, ENG, United Kingdom • 1w ago

Job Description

Quant Developer
Commodities Trading
New York City (NY), Houston (TX), or London (UK)

Our client is a global commodities trading firm, and are seeking an experienced and hands-on Quant Developer with a strong background in building and enhancing Value-at-Risk (VaR) engines or pricing engines to join their team. The successful candidate will play a critical role in the development, implementation, and continuous improvement of their risk management and pricing systems, with a particular focus on VaR engines.

Responsibilities:

Build, enhance, test and maintain quantitative models specialized for the needs of trading and risk managers, including derivatives pricing and volatility marking. The primary focus is on commodities derivatives, with exposure to other products such equity and rates derivatives.
Design and develop new VaR models using historical and factor-based approaches. Research other VaR models with emphasis on commodity market volatility and seasonality.
Contribute to the firm's effort to calculate and aggregate raw risk metrics (greeks) from different trading systems to enhance the firm's overall risk management capabilities.
Additional emphasis is on counterparty risk with projects on PFE/XVA and initial margin calculations.
Improve and extend existing risk reporting tools, including risk analysis and P&L attribution.

Requirements:

Advanced degree in a quantitative field such as Mathematics, Statistics, Financial Engineering, or a related discipline.
At least 5+ years of experience as a commodities quant or strategist or quantitative risk officer, gained in a Hedge Fund, Oil Major, Commodities Trading House or a Bank. Good knowledge of the commodities derivatives trading landscape.
Proven track record in market risk, developing and implementing VaR models, with deep knowledge of the modelling approaches and their strengths/weaknesses. Ideally, the candidate will have gained exposure to commodities specifics such as seasonality.
Expert knowledge of risk and understanding of the application of complex mathematical concepts related to Monte Carlo, options pricing and time series analysis.

Experience working with commodities specific models is a must.