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- Tasks: Lead the development of counterparty credit risk analytics and models.
- Company: Join Jefferies, a global leader in investment banking and securities.
- Benefits: Enjoy a diverse workplace with opportunities for growth and innovation.
- Why this job: Make an impact in risk management while collaborating with talented professionals.
- Qualifications: Master’s degree required; PhD preferred, with 3-5 years in risk modelling.
- Other info: Remote work options available; commitment to diversity and inclusion.
The predicted salary is between 43200 - 72000 £ per year.
Quantitative specialist for developing and managing analytics for counterparty credit risk models. Candidate will join the Risk Analytics group that partakes in model development over the full life-cycle of models: from methodology to design to local implementation and validation. The successful candidate will also provide quantitative risk analysis to support daily counterparty credit risk management.
Responsibilities
- Develop and implement analytics for counterparty credit risk management.
- Build infrastructure to consolidate counterparty credit risk models across systems.
- Perform quantitative research to implement model changes, enhancements and remediations.
- Work with stakeholders across business and functional teams during model development process.
- Create tools and dashboards which can enhance and improve the risk analysis.
- Conduct analysis on existing model short-comings and design remediation plans.
- Maintain, update and back-test risk models.
- Assess the methodologies and processes to identify potential weaknesses and the associated materiality of the risk.
Qualifications
- At least a Master’s Degree in quantitative subject; PhD Degree is a plus.
- Deep understanding of pricing and risk calculations for financial derivatives.
- Strong analytical skills required to understand quantitative models, and to translate that understanding into sustainable library design, code development and integration into IT systems.
- At least 3-5 years of experience in counterparty credit risk modeling, in particular experience working with credit simulation engines/models in a CRR and/or an XVA context.
- Strong project management and organizational skills.
- Proficient programming skills in python (other languages such as R is a plus), and strong data handling skills in SQL.
- Excellent written skills (ability to produce well-structured model documentation).
- Excellent oral communication skills to be able to interact effectively with credit risk managers and other model users.
- Knowledge of Numerix and/or Bloomberg a plus.
European Head of Risk Analytics, SVP employer: Jefferies LLC
Contact Detail:
Jefferies LLC Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land European Head of Risk Analytics, SVP
✨Tip Number 1
Familiarise yourself with the latest trends and methodologies in counterparty credit risk modelling. This will not only enhance your understanding but also allow you to engage in meaningful discussions with stakeholders during the interview process.
✨Tip Number 2
Showcase your programming skills, particularly in Python and SQL, by preparing examples of past projects or tools you've developed. Being able to discuss your technical expertise confidently can set you apart from other candidates.
✨Tip Number 3
Network with professionals in the risk analytics field, especially those who have experience at Jefferies or similar firms. Engaging with them can provide insights into the company culture and expectations, which can be invaluable during your application process.
✨Tip Number 4
Prepare to discuss how you would approach building infrastructure for consolidating counterparty credit risk models. Having a clear strategy and examples of how you've tackled similar challenges in the past can demonstrate your problem-solving abilities.
We think you need these skills to ace European Head of Risk Analytics, SVP
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your quantitative skills and experience in counterparty credit risk modeling. Emphasise your programming skills in Python and SQL, as well as any relevant project management experience.
Craft a Strong Cover Letter: In your cover letter, explain why you are passionate about risk analytics and how your background aligns with the responsibilities outlined in the job description. Mention specific projects or experiences that demonstrate your analytical skills and ability to work with stakeholders.
Showcase Your Analytical Skills: Provide examples of your previous work where you developed or enhanced risk models. Discuss any quantitative research you've conducted and how it contributed to model improvements or remediation plans.
Prepare for Technical Questions: Be ready to discuss your understanding of pricing and risk calculations for financial derivatives during interviews. Brush up on your knowledge of credit simulation engines/models and be prepared to explain your approach to model validation and back-testing.
How to prepare for a job interview at Jefferies LLC
✨Showcase Your Quantitative Skills
Be prepared to discuss your experience with quantitative models in detail. Highlight specific projects where you've developed or enhanced counterparty credit risk models, and be ready to explain the methodologies you used.
✨Demonstrate Programming Proficiency
Since strong programming skills in Python are essential for this role, ensure you can discuss your coding experience confidently. Bring examples of your work, especially any tools or dashboards you've created that relate to risk analysis.
✨Communicate Effectively
Excellent oral communication skills are crucial for interacting with credit risk managers. Practice explaining complex concepts in simple terms, as you may need to present your findings to stakeholders who may not have a technical background.
✨Prepare for Project Management Questions
Given the importance of project management in this role, think of examples from your past experiences where you've successfully managed projects. Be ready to discuss how you organised tasks, collaborated with teams, and met deadlines.
