Quantitative Risk, VP

Quantitative Analyst

The Centralized Modeling & Analytics and Operations (CMAO) team within State Street's Enterprise Risk Management (ERM) organization is looking for an experienced quantitative analyst to join our team.

The CMAO organization provides analytics based services and solutions to business units across State Street. Our mission is to create value through data driven solutions enabling State Street and our business partners to make timely and informed decisions.

This role will be part of the CMAO team focused on delivering modeling and analytics solutions to assess counter party credit risk managed by State Street Global Markets ("SSGM"). The portfolio supported includes SSGM Financing Solutions including Agency Lending, Prime Services, Secured Financing, derivatives in interest rates, FX and equity asset classes, Eligible Margin Loan in Global Credit Financing ("GCF") business. The role has significant impact on the BAU risk management as well as the regulatory CCAR requirement through complex deliverables.

This role will:

• Assume a key role in model methodology research, prototyping and determination
• Develop and build out financial models and analytics for the trading business leveraging a wide variety of mathematical and computer science methods and tools
• Advance existing codebase and propose new solutions and improvements
• Document development methodology, quantitative analysis, and implementation process
• Design and implement suitable and effective model ongoing monitoring plan including performance metrics, thresholds, and escalation plan
• Work in close partnership with control functions such as Model Risk Management, Audit, and Financial Regulatory Assurance to ensure appropriate governance and control infrastructure
• Collaborate with business users and IT partners to establish appropriate production processes within the IT infrastructure
• Timely execute CCAR deliverables
• Support regular BAU risk management activities and proactively resolve issues

Education & Preferred Qualifications

• Masters' or PhD in a quantitative discipline (Financial Mathematics, Financial Engineering, Mathematics, Statistics, Computer Science, or a related field). Experience in machine learning is a plus
• Minimum 8+ years of working experience in financial modeling field as a key contributor
• Demonstrated knowledge and experience developing or validating VaR, PFE and CVA models
• Demonstrated knowledge on derivatives, RMBS and equities pricing/modeling, yield curve building methodology, interest rate modelling
• Advanced programming skills in statistical programming environment Python and SQL are required
• Familiarity with CCAR regulatory frameworks and the corresponding requirements is a plus
• Self-motivated and attention to detail
• Demonstrated ability to work independently on complex projects as well as the ability to be a team player in a fast-paced, high-energy level environment
• Strong verbal and written communication skills, with ability to articulate ideas, analysis and complex concepts effectively to broad audiences
• Competence and confidence to gain credibility and collaborate for success across the organization

Salary Range:

$120,000 - $202,500 Annual

The range quoted above applies to the role in the primary location specified. If the candidate would ultimately work outside of the primary location above, the applicable range could differ.

Employees are eligible to participate in State Street's comprehensive benefits program, which includes: our retirement savings plan (401K) with company match; insurance coverage including basic life, medical, dental, vision, long-term disability, and other optional additional coverages; paid-time off including vacation, sick leave, short term disability, and family care responsibilities; access to our Employee Assistance Program; incentive compensation including eligibility for annual performance-based awards (excluding certain sales roles subject to sales incentive plans);

and, eligibility for certain tax advantaged savings plans.