We are looking for a new member to join our cross-asset team in the Model Risk Governance and Review group which is responsible for end-to-end model risk management across the firm for electronic trading, strategic indices and prime services models.

As a Quant Modeling Analyst in our Model Risk Governance and Review group, you will assess and help mitigate the model risk of complex models used in the context of valuation, risk measurement, the calculation of capital, and more broadly for decision-making purposes. Additionally, you will have an opportunity for exposure to a variety of business and functional areas and will work closely with model developers and users.

Job responsibilities

Evaluate conceptual soundness of model specifications, reasonableness of assumptions, reliability of inputs, completeness of testing, correctness of implementation, and suitability and comprehensiveness of performance metrics and risk measures  Perform independent testing of models by replicating or building benchmark models Design and implement experiments to measure the potential impact of model limitations, parameter estimation errors, and deviations from model assumptions; compare model outputs with empirical evidence or outputs from model benchmarks Evaluate the risks posed by non-transparent model parameters and/or non-linear relationships, and suggest ways to mitigate such risks Document the model review findings and communicate them to stakeholders Serve as the first point of contact for model governance related inquiries for the coverage area, and help identify and escalate issues to ensure that their resolutions are sound and timely Provide guidance on the appropriate usage of models to model developers, users, and other stakeholders in the firm Stay abreast of the ongoing performance testing outcomes for models used in the coverage area, and communicate those outcomes to stakeholders  Maintain the model inventory and model metadata for the coverage area  Maintain the pace with the latest developments in coverage area in terms of products, markets, models, risk management practices, and industry standards

Required qualifications, capabilities, and skills

Master’s or PhD in a quantitative discipline such as Mathematics, Physics, Engineering, Computer Science, Economics or Finance Strong analytical and problem-solving skills Excellence in probability theory, stochastic processes, statistics, partial differential equations, and numerical analysis Risk and control-oriented mindset: ability to ask incisive questions, assess materiality of model issues, and escalate issues appropriately Excellent communication skills (written and verbal) Good understanding of option pricing theory (i.e. quantitative models for pricing and hedging derivatives option). Good coding skills, for example in C/C++ or Python

Preferred qualifications, capabilities, and skills

Data and numeric programming (NumPy, SciPy, Pandas, etc.) Database interfacing, data management and (pre-)processing (kdb, q, SQL) Experience of working with tensorflow and other ML packages