VP, AI & Data Analytics

A leading International Investment Bank in NYC is seeking a Vice President to join its AI focused Market Risk team within their Risk Modeling function. This is an exciting opportunity to help drive the firm's data analytics and AI capabilities while supporting the development and enhancement of key market risk models used across the trading business.

This role will be highly visible, partnering with Front Office, Risk Management, Technology, and Data teams to develop innovative analytical solutions, improve market risk infrastructure, and enhance the firm's risk framework. You will work closely with senior stakeholders while helping to shape the use of advanced analytics, AI technologies, and large-scale data solutions within a growing quantitative risk organization.

Responsibilities

• Lead the development and implementation of AI-driven solutions to enhance market risk data processing, historical time series construction, and data management workflows.
• Build and maintain scalable frameworks for data quality monitoring, anomaly detection, exception management, and automated remediation.
• Support the development, enhancement, and ongoing monitoring of trading book risk models including VaR, Stressed VaR, sensitivity analysis, and stress testing.
• Design statistical and analytical approaches to evaluate model performance, data integrity, and analytical outputs across large financial datasets.
• Develop prototype and production-ready analytical solutions using structured and unstructured market risk data.
• Partner with model validation and governance teams to ensure models and analytical frameworks comply with internal standards and regulatory expectations.
• Collaborate with Technology teams to integrate analytical solutions into production environments and improve operational efficiency.

Qualifications

• At least 5 years of experience within data analytics, artificial intelligence, machine learning, quantitative risk, or a related function.
• Advanced degree in Mathematics, Statistics, Computer Science, Data Science, Financial Engineering, or a related quantitative discipline.
• Strong Python programming skills and experience working with large structured and unstructured datasets.
• Understanding of market risk methodologies including VaR, sensitivities, risk factor modeling, and stress testing.
• Experience developing quantitative, statistical, or analytical models and familiarity with the model development lifecycle.
• Strong analytical and problem-solving skills with the ability to translate complex information into actionable insights.
• Excellent communication skills and experience partnering with cross-functional stakeholders across Risk, Technology, and business teams