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- Tasks: Validate and approve quantitative models, ensuring accuracy and soundness.
- Company: Join a leading financial institution focused on model risk management.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Make an impact by enhancing model performance and managing risks in finance.
- Qualifications: MSc or PhD in a quantitative field; experience in model validation preferred.
- Other info: Collaborative team environment with a focus on innovation and automation.
The predicted salary is between 36000 - 60000 £ per year.
Job Description
The primary mandate of the Model Validation team is to manage model risk across a broad range of business areas, including models used for derivatives valuation, market and credit risk management, liquidity, and capital computations. The team is responsible for independently reviewing models to ensure theoretical soundness, implementation accuracy, and appropriate use. The role involves evaluating model performance, identifying limitations, and helping stakeholders understand the associated risks.
Key Responsibilities
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Perform independent validation and approval of quantitative models, raising and managing model validation findings.
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Conduct annual reviews and revalidations of existing models.
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Provide effective challenge to model assumptions, mathematical formulations, and implementation methodologies.
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Assess and quantify model risk to inform stakeholders and contribute to compensating control development.
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Contribute to strategic and cross-functional initiatives within the Model Risk function.
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Oversee ongoing model performance monitoring, including benchmarking, process verification, and outcome analysis.
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Communicate validation results, model limitations, and uncertainties to stakeholders and management.
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Contribute to automation and efficiency initiatives, including applications of AI and process optimization.
Qualifications
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MSc or preferably PhD in a quantitative discipline (e.g., Physics, Mathematics, Computer Science, Financial Engineering, Statistics).
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Strong understanding of Value-at-Risk (VaR) computation frameworks and Counterparty Credit Risk (CCR) modelling.
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Experience in model validation or development, particularly within risk or liquidity modelling contexts.
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Proficiency in Python (preferred) or similar quantitative programming languages.
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Strong analytical and communication skills, with the ability to provide practical solutions to complex challenges.
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Demonstrated ability to work collaboratively within a team-oriented environment.
Additional Skills – Liquidity Modelling in Investment Banking
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Deep understanding of liquidity risk frameworks and internal liquidity stress testing (ILST) methodologies.
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Experience validating or developing liquidity models, including cash flow projections, liquidity coverage ratio (LCR), and net stable funding ratio (NSFR) frameworks.
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Familiarity with regulatory expectations for liquidity risk management (e.g., Basel III, PRA, FED, or ECB guidelines).
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Ability to assess model performance under stressed conditions and evaluate model assumptions around funding profiles, behavioral deposits, and contingency funding.
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Knowledge of balance sheet and treasury modelling, including funding concentration and intraday liquidity risk.
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Experience working with liquidity data, scenario analysis, and backtesting of liquidity models.
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Strong quantitative and programming skills for implementing and testing liquidity models efficiently.
McGregor Boyall is an equal opportunity employer and do not discriminate on any grounds.
Quantitative Analyst - Model Validation employer: McGregor Boyall
Contact Detail:
McGregor Boyall Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Quantitative Analyst - Model Validation
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect on LinkedIn. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Prepare for interviews by brushing up on your technical skills and understanding the latest trends in model validation. Practice common interview questions and be ready to discuss your experience with VaR and CCR modelling.
✨Tip Number 3
Showcase your analytical skills during interviews. Be prepared to solve problems on the spot or discuss how you've tackled complex challenges in the past. This is your chance to shine!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Quantitative Analyst - Model Validation
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your relevant experience in model validation and quantitative analysis. We want to see how your skills align with the job description, so don’t be shy about showcasing your expertise in areas like Value-at-Risk and liquidity modelling.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you’re passionate about model validation and how your background makes you a perfect fit for our team. We love seeing enthusiasm and a clear understanding of the role.
Showcase Your Technical Skills: Since this role requires proficiency in Python or similar programming languages, make sure to mention any relevant projects or experiences. We’re keen on seeing how you’ve applied your technical skills in real-world scenarios, especially in risk modelling.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about our company culture and values!
How to prepare for a job interview at McGregor Boyall
✨Know Your Models Inside Out
Make sure you have a solid understanding of the models you'll be validating. Brush up on Value-at-Risk (VaR) computation frameworks and Counterparty Credit Risk (CCR) modelling. Being able to discuss these concepts confidently will show that you're not just familiar with the theory, but also understand their practical applications.
✨Prepare for Technical Questions
Expect to face technical questions related to model validation and liquidity risk frameworks. Review your knowledge on liquidity coverage ratios (LCR) and net stable funding ratios (NSFR). Practising how to explain complex concepts in simple terms can help you communicate effectively during the interview.
✨Showcase Your Programming Skills
Since proficiency in Python or similar programming languages is key, be ready to discuss your experience with coding. Consider preparing examples of how you've used programming to solve quantitative problems or automate processes. This will demonstrate your technical capabilities and problem-solving skills.
✨Emphasise Team Collaboration
Model validation often requires teamwork, so highlight your ability to work collaboratively. Share examples of past experiences where you contributed to team projects or cross-functional initiatives. This will illustrate your interpersonal skills and your fit within a team-oriented environment.
