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For Companies At a Glance
- Tasks: Validate and monitor risk models, ensuring accuracy and compliance with regulations.
- Company: Join ICE Clear Europe, a leading clearing house in the financial sector.
- Benefits: Enjoy a collaborative work environment with opportunities for mentorship and professional growth.
- Why this job: Gain technical expertise and broader exposure in a flat organisational structure.
- Qualifications: MSc/PhD in Mathematics or related field; experience in model validation and quantitative analysis required.
- Other info: Ideal for those passionate about risk management and looking to make an impact.
The predicted salary is between 48000 - 72000 £ per year.
Job Description
Job Purpose
ICE Clear Europe is seeking a Quantitative Risk Manager to join its Model Risk Management team. This role is responsible for validating and monitoring risk models used in the clearing house, ensuring their accuracy, robustness, and compliance with regulatory standards. The position involves end-to-end model risk assessment across initial margin, add-ons, and stress testing frameworks, with a focus on market, credit, and liquidity risk. This is an exciting opportunity for a technical expert looking for broader model and management exposure in a collaborative and flat organizational structure.
Responsibilities
- Conduct independent validation of risk and pricing models and review of stress testing frameworks, including conceptual soundness, assumption reasonableness, and performance benchmarking.
- Continuously monitor model performance and review first-line risk management monitoring approaches.
- Document validation findings, communicate risks, and recommend improvements.
- Provide guidance on model usage and act as a key stakeholder liaison for new models and changes.
- Stay updated on evolving market practices, regulatory requirements, and quantitative methodologies.
- Mentor and support junior team members.
Knowledge and Experience
- Degree (MSc/PhD preferred) in Mathematics, Statistics, Quantitative Finance, or related field.
- Extensive experience in model validation, quantitative analysis, or risk analytics.
- Strong knowledge of market, credit, and liquidity risk frameworks.
- Proficiency in Python (NumPy, Pandas, etc.) and SQL for data analysis.
- Strong understanding of option pricing theory and statistical risk modelling techniques (VaR, Backtesting, Stress Testing).
- Excellent verbal and written communication skills.
Preferred
- Industry certifications (PRM, FRM, CFA).
- Experience in a clearing house, trading firm, or similar financial institution.
- Familiarity with SR 11-7 model risk guidelines and exchange-traded derivatives.
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Model Risk Manager employer: ICE
Contact Detail:
ICE Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Model Risk Manager
✨Tip Number 1
Network with professionals in the quantitative finance and risk management fields. Attend industry conferences, webinars, or local meetups to connect with people who work in similar roles. This can help you gain insights into the company culture and potentially get a referral.
✨Tip Number 2
Familiarise yourself with the latest regulatory standards and market practices related to model risk management. Being well-versed in these areas will not only boost your confidence during interviews but also demonstrate your commitment to staying updated in this fast-evolving field.
✨Tip Number 3
Prepare to discuss specific examples of your experience with model validation and quantitative analysis. Think about challenges you've faced and how you overcame them, as well as any improvements you've implemented in previous roles. This will showcase your problem-solving skills and technical expertise.
✨Tip Number 4
Brush up on your Python and SQL skills, especially focusing on libraries like NumPy and Pandas. Consider working on personal projects or contributing to open-source projects that involve data analysis or risk modelling to strengthen your practical knowledge and make your application stand out.
We think you need these skills to ace Model Risk Manager
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in model validation, quantitative analysis, and risk analytics. Emphasise your proficiency in Python and SQL, as well as any industry certifications you hold.
Craft a Strong Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss your understanding of market, credit, and liquidity risk frameworks, and how your background aligns with the responsibilities outlined in the job description.
Showcase Your Technical Skills: Provide specific examples of your experience with risk models, stress testing frameworks, and statistical risk modelling techniques. Mention any projects where you successfully validated models or improved risk management processes.
Proofread Your Application: Before submitting, carefully proofread your application materials to ensure there are no grammatical errors or typos. A polished application reflects your attention to detail, which is crucial for a Model Risk Manager.
How to prepare for a job interview at ICE
✨Showcase Your Technical Expertise
As a Model Risk Manager, you'll need to demonstrate your proficiency in quantitative analysis and model validation. Be prepared to discuss specific models you've worked on, the methodologies you used, and how you ensured their compliance with regulatory standards.
✨Understand the Regulatory Landscape
Familiarise yourself with the SR 11-7 model risk guidelines and other relevant regulations. During the interview, highlight your knowledge of these standards and how they influence model risk management practices in a clearing house environment.
✨Prepare for Technical Questions
Expect to face technical questions related to market, credit, and liquidity risk frameworks. Brush up on concepts like VaR, backtesting, and stress testing, and be ready to explain how you've applied these techniques in your previous roles.
✨Demonstrate Communication Skills
Excellent verbal and written communication skills are crucial for this role. Practice articulating complex ideas clearly and concisely, as you'll need to communicate validation findings and recommendations effectively to both technical and non-technical stakeholders.
