VP, Front Office Market Risk & GenAI Analytics

Citi; New York (NY); Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Hybrid work model

Competitive compensation

Job summary

Citi is actively seeking a Senior Quantitative Analyst in New York to join the Front Office Market Risk team at the VP level. You will build analytics and GenAI-enabled applications, collaborating with traders and risk managers to inform hedging and capital decisions.

The role requires deep expertise in VaR, stress testing, and large-scale risk analytics, with a focus on scalable Python-based solutions and regulatory-compliant methodologies. Hybrid work arrangements apply.

Qualifications

• Ten or more years of quantitative modeling experience in market risk.
• Advanced Python with pandas and numpy for large datasets; SQL & version control.

Responsibilities

• Build analytical tools and GenAI-enabled applications for real-time market risk visibility.
• Design and deploy AI-assisted workflows integrating LLMs with risk analytics platforms.
• Develop scalable GenAI pipelines with agentic workflows and production-grade interfaces.
• Collaborate with traders and risk managers to analyze and improve risk models and frameworks.
• Contribute production-quality Python code to in-house analytics libraries.
• Coordinate end-to-end delivery of market risk and GenAI initiatives with stakeholders.

Skills

Python programming

Pandas

NumPy

SQL

Git/Bitbucket

VaR & stress testing

Regulatory frameworks

GenAI concepts

Communication

Quantitative modeling

Education

PhD or MSc in statistics/math/quantitative finance

Tools

Git/Bitbucket

Job description

Citi is actively seeking a Senior Quantitative Analyst in New York to join the Front Office Market Risk team at the VP level. You will build analytics and GenAI-enabled applications, collaborating with traders and risk managers to inform hedging and capital decisions.

The role requires deep expertise in VaR, stress testing, and large-scale risk analytics, with a focus on scalable Python-based solutions and regulatory-compliant methodologies. Hybrid work arrangements apply.