Quant Modeling Analyst

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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 valuation, risk measurement, the calculation of capital, and broader decision-making processes. You will also have exposure to various business and functional areas and work closely with model developers and users.

Job responsibilities

1. Evaluate the conceptual soundness of model specifications, reasonableness of assumptions, reliability of inputs, completeness of testing, correctness of implementation, and the suitability and comprehensiveness of performance metrics and risk measures.
2. Perform independent testing of models by replicating or building benchmark models.
3. 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.
4. Evaluate risks posed by non-transparent model parameters and/or non-linear relationships, and suggest mitigation strategies.
5. Document model review findings and communicate them to stakeholders.
6. Serve as the first point of contact for model governance inquiries within the coverage area, and help identify and escalate issues for timely resolution.
7. Provide guidance on the appropriate usage of models to developers, users, and stakeholders.
8. Monitor ongoing performance testing outcomes for models in the coverage area and communicate these to stakeholders.
9. Maintain the model inventory and metadata for the coverage area.
10. Stay updated on developments in products, markets, models, risk management practices, and industry standards relevant to the coverage area.

Required qualifications, capabilities, and skills

1. Master’s or PhD in a quantitative discipline such as Mathematics, Physics, Engineering, Computer Science, Economics, or Finance.
2. Strong analytical and problem-solving skills.
3. Proficiency in probability theory, stochastic processes, statistics, partial differential equations, and numerical analysis.
4. Risk and control-oriented mindset: ability to ask incisive questions, assess model issues\’ materiality, and escalate appropriately.
5. Good understanding of option pricing theory and quantitative models for pricing and hedging derivatives.
6. Proficiency in coding, e.g., in C/C++ or Python.

Preferred qualifications, capabilities, and skills

1. Experience with data and numeric programming (NumPy, SciPy, Pandas).
2. Experience with database interfacing, data management, and preprocessing (kdb, q, SQL).
3. Experience working with TensorFlow and other machine learning packages.

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