Quant Model Risk Associate Commodities

JPMorganChase; Greater London; On-site; GBP 65,000 - 90,000; Full time

Job summary

A leading global financial institution is seeking a Quant Model Risk Associate to join their Commodities team. This role involves assessing and mitigating risks associated with complex models used in valuation and capital calculation. Candidates should have strong quantitative skills, including a Master's or PhD in a relevant field, and proficiency in programming languages like Python and C/C++. The position offers an opportunity to challenge models and ensure they meet rigorous standards, supporting the firm's commitment to effective risk management.

Qualifications

• Excellence in probability theory and statistics.
• Inquisitive nature with strong communication skills.
• Good coding skills in C/C++ and Python.

Responsibilities

• Analyze conceptual soundness of complex pricing models.
• Provide guidance on model usage for the business.
• Evaluate model performance regularly.

Skills

Probability theory

Stochastic processes

Statistics

Numerical analysis

C / C++

Python

Option pricing theory

Education

MSc or PhD in a quantitative discipline

Tools

R

Matlab

MongoDB

SAS

Job description

Description

We are looking for a new member to join our Commodities team in the Model Risk Governance and Review Group which is responsible for end-to-end model risk management across the firm. We adopt a comprehensive model risk management approach assessing models within their usage context and based on relevant success criteria. Our role involves identifying limitations communicating them effectively and assisting model users in the design compensating controls. This approach requires strong technical skills and business understanding offering an excellent opportunity for skill development setting us apart from typical validation teams.

As a Quant Model Risk Associate within our Risk Management team you will be responsible for assessing and mitigating the risks associated with complex models used for valuation risk measurement capital calculation and decision‑making purposes. This role also provides the opportunity to gain exposure to various business and functional areas as well as collaborate closely with model developers and users. Unlike the theoretical empowerment seen at some banks our team is truly empowered to challenge front office models ensuring they meet rigorous standards before being used in production. The bank genuinely values our role in providing effective independent challenge prioritizes model adequacy and fitness for purpose over business opportunities when needed.

Job responsibilities

• Carry out model reviews: analyze conceptual soundness of complex pricing models engines and reserve methodologies; assess model behavior and suitability of pricing models / engines for particular products / structures
• Provide guidance on model usage and act as first point of contact for the business on all new models and changes to existing models
• Develop and implement alternative model benchmarks and compare the outcome of various models; design model performance metrics
• liaise with model developers, Risk and Valuation Control Groups and provide guidance on model risk
• Evaluate model performance on a regular basis

Required qualifications, capabilities and skills

• Excellence in probability theory, stochastic processes, statistics, partial differential equations and numerical analysis
• MSc, PhD or equivalent in a quantitative discipline
• Inquisitive nature, ability to ask right questions and to escallate issues
• Excellent communication skills (written and verbal)
• Good understanding of option pricing theory
• Good coding skills for example in C / C++, Python

Preferred qualifications, capabilities and skills

• Experience with commodity derivatives
• Experience in a front‑office or model risk quantitative role

Key Skills

• Python
• C / C++
• Fortran
• R
• Data Mining
• Matlab
• Data Modeling
• Laboratory Techniques
• MongoDB
• SAS
• Systems Analysis
• Dancing

Employment Type: Full-Time

Experience: years

Vacancy: 1