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- Tasks: Lead the Counterparty Credit Risk modelling function and manage the entire modelling lifecycle.
- Company: Join a prestigious Global Investment Bank at the forefront of finance.
- Benefits: Enjoy a permanent position with opportunities for professional growth and development.
- Why this job: Be part of a dynamic team, driving innovation in risk analytics and making a real impact.
- Qualifications: Master’s or PhD in a quantitative field; expertise in Python, R, and SQL required.
- Other info: Ideal for those passionate about quantitative analysis and model development.
The predicted salary is between 72000 - 108000 £ per year.
A senior Quantitative Specialist is sought after by a Global Investment Bank to take ownership of their Counterparty Credit Risk (CRR) modelling function, as part of the wider global Risk Analytics group. In this role, you will be responsible for managing the end-to-end modelling lifecycle, being responsible for methodology, model design and development, through to implementation and validation, helping support local Counterparty Credit Risk Management.
This will be a multi-functional role, with responsibility for building and maintaining the modelling infrastructure and ecosystem, as well as undertaking quantitative research to keep models up to date ensuring the business have access to accurate analytics. You will work closely with the business and other quantitative specialists for a cohesive model development process, including the implementation of highly accessible tools and dashboards for users to effectively undertake risk analysis.
To be successful, you will demonstrate:
- Minimum of a Master’s degree in the quantitative field, preferably having achieved a PhD
- A strong background in Quantitative Analysis and Model Development, with an in-depth understanding of pricing and risk calculations, particularly for Counterparty Credit Risk
- Technical expertise in Python, R and SQL
- Knowledge of integrating quantitative libraries and models into IT systems
- Excellent communication skills and a collaborative mindset to ensure effective partnership with the business and other Quantitative specialists
If you are a Quantitative Specialist with SME knowledge in Counterparty Credit Risk modelling looking for your next challenge with a rapidly expanding Investment Bank, please do apply!
Head of Risk Analytics - Quantitative Finance, Counterparty Credit Risk, Model Development, Python employer: Cornwallis Elt Ltd
Contact Detail:
Cornwallis Elt Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Head of Risk Analytics - Quantitative Finance, Counterparty Credit Risk, Model Development, Python
✨Tip Number 1
Network with professionals in the quantitative finance field, especially those who have experience in Counterparty Credit Risk. Attend industry conferences or webinars to connect with potential colleagues and learn about the latest trends and challenges in risk analytics.
✨Tip Number 2
Showcase your technical skills by contributing to open-source projects or creating your own projects using Python, R, or SQL. This not only demonstrates your expertise but also gives you practical examples to discuss during interviews.
✨Tip Number 3
Stay updated on regulatory changes and best practices in Counterparty Credit Risk management. Being knowledgeable about current regulations can set you apart and show your commitment to the field.
✨Tip Number 4
Prepare for interviews by practising common quantitative finance case studies and model validation scenarios. This will help you articulate your thought process and problem-solving skills effectively when discussing your approach to risk analytics.
We think you need these skills to ace Head of Risk Analytics - Quantitative Finance, Counterparty Credit Risk, Model Development, Python
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in Quantitative Analysis and Model Development. Emphasise your technical skills in Python, R, and SQL, as well as any relevant projects or roles that demonstrate your expertise in Counterparty Credit Risk.
Craft a Compelling Cover Letter: Write a cover letter that specifically addresses the requirements of the Head of Risk Analytics position. Discuss your academic background, particularly if you have a Master’s or PhD, and how your experience aligns with the responsibilities outlined in the job description.
Showcase Your Technical Skills: In your application, provide examples of how you've integrated quantitative libraries and models into IT systems. Mention any tools or dashboards you've developed for risk analysis to demonstrate your hands-on experience.
Highlight Collaboration Experience: Since the role requires effective partnership with the business and other specialists, include examples of past collaborative projects. This will show your ability to work in a multi-functional environment and communicate effectively with diverse teams.
How to prepare for a job interview at Cornwallis Elt Ltd
✨Showcase Your Technical Skills
Make sure to highlight your expertise in Python, R, and SQL during the interview. Be prepared to discuss specific projects where you've used these languages for model development or quantitative analysis.
✨Demonstrate Your Understanding of Counterparty Credit Risk
Since this role focuses on Counterparty Credit Risk modelling, be ready to explain your knowledge of pricing and risk calculations. Discuss any relevant experience you have in this area to show that you understand the complexities involved.
✨Prepare for Collaborative Scenarios
Given the emphasis on collaboration with other quantitative specialists and business units, think of examples where you've successfully worked in a team. Highlight your communication skills and how you’ve contributed to cohesive model development processes.
✨Discuss Your Research and Model Validation Experience
Be prepared to talk about your experience with quantitative research and model validation. Share insights on how you've kept models up to date and ensured accuracy in analytics, as this will demonstrate your proactive approach to risk management.
