Quantitative Risk AVP — Modeling & Analytics Leader

State Street; Boston (MA); On-site; USD 90,000 - 158,000; Full time

Benefits offered by this job

401K plan

Medical, dental, vision insurance

Paid time off

Employee Assistance Program

Performance-based awards

Job summary

State Street seeks an experienced quantitative analyst to join the CMAO team within ERM. You will develop and validate financial models for counterparty credit risk, contribute to VaR, PFE, and CVA work, and collaborate with IT and control functions to meet CCAR obligations.

The role emphasizes rigorous analysis, production of robust monitoring, documentation, and timely CCAR deliverables, with opportunities to apply advanced machine learning techniques and improve the modeling framework.

Qualifications

• Master's or PhD in a quantitative discipline (Financial Mathematics, Financial Engineering, Mathematics, Statistics, Computer Science, or related).
• Experience in financial modeling as a key contributor (2–4 years).
• Knowledge of VaR, PFE and CVA models and derivatives pricing.
• Programming skills in Python and SQL; CCAR familiarity a plus.
• Strong communication and independent working abilities.

Responsibilities

• Research and prototype model methodologies for counterparty credit risk.
• Develop and build financial models and analytics for trading, including risk measures such as VaR, PFE and CVA.
• Improve existing codebase and propose new solutions.
• Document methodology and implementation; monitor models with metrics and thresholds.
• Collaborate with Model Risk Management, Audit, and IT to ensure governance and controls.
• Deliver CCAR-related outputs in a timely manner.

Skills

Machine learning

Statistical modeling

Quantitative analysis

Education

Master's or PhD in quantitative discipline

Tools

Python

SQL

Job description

State Street seeks an experienced quantitative analyst to join the CMAO team within ERM. You will develop and validate financial models for counterparty credit risk, contribute to VaR, PFE, and CVA work, and collaborate with IT and control functions to meet CCAR obligations.

The role emphasizes rigorous analysis, production of robust monitoring, documentation, and timely CCAR deliverables, with opportunities to apply advanced machine learning techniques and improve the modeling framework.