About the Team Our team oversees the risk posed by models in the fixed income investment management, interest rate and mortgage risk hedging as well as corporate ML initiatives. We are data scientists, risk managers, and early adopters of technology tasked with mitigating risk from AI/ML solutions while, at the same time, providing guidance around the appropriate development of such models.

About the Group

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

About the Team

Our team oversees the risk posed by models in the fixed income investment management, interest rate and mortgage risk hedging as well as corporate ML initiatives. We are data scientists, risk managers, and early adopters of technology tasked with mitigating risk from AI/ML solutions while, at the same time, providing guidance around the appropriate development of such models.

Why Join Us?

We offer a dynamic environment for professional development in the field of Fixed income portfolio management as well as emerging  areas of AI and ML. In addition, we also offer you the opportunity to:

• Review a large variety of ML and AI applications in banking, allowing you to learn in a real-world environment
• Get access to experts, state of the art infrastructure, and deep scientific knowledge base built up over several decades of work

About the Role

The Analyst level position requires the successful candidate to:

1) quantitatively evaluate complex math, statistics and data science problems applied to finance and banking use cases, and

2) build benchmark models in the process of evaluating (1).

A significant portion of the successful candidate’s time is likely to be spent on reviewing data driven models. Such models are currently used to detect fraud, develop sentiment signals, detect fraudulent patterns in data, assess credit risk limits and assist screening of candidates and clients amongst other applications.

Responsibilities:

• Evaluate conceptual soundness of data intensive ML model use cases; reasonableness of assumptions; reliability of inputs; completeness of testing performed; correctness of implementation; and suitability / comprehensiveness of performance metrics and risk measures
• Design and implement experiments to measure the potential impact of model limitations, parameter estimation errors or deviations from model assumptions; compare model outputs with empirical evidence and/or outputs from model benchmarks
• Evaluate the risk posed specifically by non-transparent and non-linear models, and suggest ways to mitigate such risks
• Liaise with front office, Finance and Risk professionals to monitor usage and performance of the models
• Evaluate market conditions under which a given model is likely to break down
• Identify market risks most relevant to the bank’s various lines of business
• Cogently document findings
• Work with MRGR review teams in other locations, to ensure high quality model reviews, re-reviews, minor model enhancements, and model usage reviews in compliance with firm’s Estimation policies and procedures
• Work with other partners such as the MRGR COO team, Risk and Control functions, Finance, Technology and Audit to ensure that key model risks are understood, recorded, monitored and managed with appropriate compensating controls (when applicable).
• Supports Model performance Monitoring

Requirements:

• A Ph.D. or Master’s degree in a quantitative field such as Finance, Economics, Math, Physics or Engineering is required
• Candidates with significantly more experience may be considered for more senior roles
• The candidate is expected to have a good understanding of machine learning models. Experience with large data sets and training ML models is required
• Understanding of statistics / econometrics
• Thorough knowledge of at least one programming language such as Matlab, R, Python, C/C++, etc. is required
• Communication skills are important since the role requires interacting with many groups across the firm as well as producing documents for both internal and external (regulatory) consumption