Risk Model Review Associate London 2023

The Model Risk Governance and Review Group (MRGR) oversees model risk at the firm, conducts independent model reviews, and provides guidance around a model’s appropriate usage. The Group is tasked with assessing and mitigating the risk posed by usage of all types of quantitative models in the firm.

Employer Description

Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management.

We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. In accordance with applicable law, we make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as any mental health or physical disability needs.

Job summary

Financial Institutions routinely use models for a broad range of activities including credit underwriting, valuing financial instruments, measuring and managing risk, assessing the adequacy of reserves and capital resources, and many other applications. Model Risk arises from the potential adverse consequences of making decisions based on incorrect or misused model outputs and reports, leading to financial loss, poor business decision making, or reputational damage.

The Group (MRGR) is responsible for conducting model validation to help identify, measure, and mitigate Model Risk. The objective is to ensure that models are used appropriately in the business context and that model users are aware of the models' strengths and limitations and how these can impact their decisions.

The aim of the MRGR VaR group is to perform reviews of the Firm’s VaR models and to ensure the way in which JP Morgan quantifies, monitors and manages risk is robust. This role involves examining the behavior of these risk models by examining their performance for different exposures and in varying market conditions. It entails exposure to a broad range of models, including the pricing models used to value derivatives, and statistical models of the risk factors used to estimate possible market scenarios.

MRGR carries out model validation activities and works closely with Risk, Finance and LOB professionals to review findings, and to perform on-going model risk measurement and risk mitigation strategies.

Core responsibilities

The successful candidate will be a member of MRGR and will work on the validation of VaR models used in connection with regulatory capital measurement.

The successful candidate will carry out a number of model validation activities, including

• Model reviews: evaluate conceptual soundness of model specification; reasonableness of assumptions and reliability of inputs; completeness of testing performed to support the correctness of the implementation; robustness of numerical aspects; suitability and comprehensiveness of performance metrics and risk measures associated with use of a model.
• Model risk measurement: design and implement experiments to measure the potential impact of model limitations, parameter estimation error or deviations from model assumptions; compare model outputs with empirical evidence and/or outputs from model benchmarks.
• Identify market conditions under which a model’s performance may degrade.
• Liaise with FO, Finance and Risk professionals to monitor usage and performance of the models and syndicate the findings of model validation.
• Document and explain review findings to model developers and risk management.

Essential skills, experience, and qualifications

An advanced degree in a subject such as Applied Mathematics, Economics, Physics, Statistics, Engineering or similar.

Deep understanding of probability theory, econometrics, statistics, and numerical methods.

Experienced in one or more programming languages and in working with complex data sets.

Excellent analytical and problem solving abilities.

Inquisitive nature, ability to ask right questions and to escalate issues. Risk & Control mindset.

Excellent communication skills (written and verbal).