Quant Modeling Analyst

TN United Kingdom; London; On-site; GBP 50,000 - 90,000; Full time

Job summary

An established industry player is seeking a Quant Modeling Analyst to join their dynamic cross-asset team. This exciting role involves assessing and mitigating model risks associated with electronic trading and strategic indices. You will engage with model developers and users, ensuring the integrity of complex models used in valuation and risk measurement. As a key contributor, you will design experiments, evaluate risks, and provide guidance on model usage. This position offers a unique opportunity to stay at the forefront of risk management practices and contribute to critical decision-making processes within the firm.

Qualifications

• Master’s or PhD in a quantitative discipline required.
• Strong analytical skills and coding proficiency in C/C++ or Python.

Responsibilities

• Evaluate model specifications and assumptions for soundness.
• Perform independent testing and document findings for stakeholders.

Skills

Analytical Skills

Problem-Solving Skills

Probability Theory

Stochastic Processes

Statistics

Partial Differential Equations

Numerical Analysis

Risk Assessment

Option Pricing Theory

Coding (C/C++ or Python)

Education

Master’s or PhD in Mathematics

Master’s or PhD in Physics

Master’s or PhD in Engineering

Master’s or PhD in Computer Science

Master’s or PhD in Economics

Master’s or PhD in Finance

Tools

NumPy

SciPy

Pandas

SQL

TensorFlow

kdb

q

Job description

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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.