Cross Asset XVA Quantitative Analyst - Vice President
Are you a highly skilled Quantitative Analyst seeking to tackle some of the most complex financial problems within a global institution? Citi's Markets Quantitative Analysis (MQA) group is looking for talented individuals to join our Central XVA team. This role offers a unique opportunity to contribute to innovative solutions for cross-asset XVA functionality, directly impacting our trading businesses, control functions, and international client base.
Team/Role Overview:
The Central XVA team, a specialized group within Markets Quantitative Analysis (MQA), is dedicated to building innovative solutions for complex financial problems. We focus on critical XVA functionality that cuts across various asset classes, working to enhance pricing, risk management, and regulatory compliance. This is a small, high-impact team where your contributions will directly influence crucial cross-asset XVA analytics.
What You'll Do:
Create and support analytics for Markets Front Office XVA across multiple asset classes, leveraging probability theory, financial mathematics, and numerical techniques.
Implement these analytics in C++, also utilizing Python for development.
Provide essential support to trading desks for XVA-related inquiries and models.
Collaborate closely with other MQA teams to ensure consistency and share best practices across the organization.
Work in partnership with control functions to ensure appropriate governance and control infrastructure for all XVA analytics.
Contribute to building a culture of responsible finance, good governance and supervision, expense discipline, and ethics.
Appropriately assess risk/reward of transactions when making business decisions and ensure that all team members understand the need to do the same, demonstrating proper consideration for the firm’s reputation.
What We'll Need From You:
Relevant experience in a comparable quantitative modeling role in the financial sector. XVA-related experience is especially valuable.
Strong knowledge of financial products and related quantitative methods, especially Monte Carlo simulation techniques.
Clear and concise written and verbal communication skills.
An MSc or PhD degree in a quantitative subject.
Strong programming skills, preferably in C++.
What We Can Offer You:
High-Impact Role: Work on important XVA functionality that cuts across multiple asset classes, directly influencing trading decisions and control functions.
Advanced Quantitative Work: Engage in complex analytics, utilizing probability theory, financial mathematics, and numerical techniques like Monte Carlo simulation.
Technical Excellence: Develop and implement solutions primarily in C++ and Python, contributing to robust and performant analytics.
Collaborative Environment: Work closely with trading desks, other MQA teams, and control functions, fostering a deep understanding of the business and analytical needs.
Career Growth: This role offers significant opportunities for professional development and advancement within Citi's global Markets Quantitative Analysis (MQA) group.
Comprehensive Benefits: As part of a leading global financial institution, you will receive a competitive base salary and a comprehensive benefits package designed to support your well-being and professional growth.
If you are a highly motivated Quantitative Analyst with a passion for XVA and strong programming skills, eager to contribute to cutting-edge cross-asset analytics, we encourage you to apply.
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Job Family Group:
Risk Management------------------------------------------------------
Job Family:
Model Development and Analytics------------------------------------------------------
Time Type:
Full time------------------------------------------------------
Most Relevant Skills
Analytical Thinking, Credible Challenge, Data Analysis, Governance, Policy, Procedure, and Regulation, Risk Management Lifecycle.------------------------------------------------------
Other Relevant Skills
For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
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