POSITION SUMMARY

The Senior Specialist, Risk Modeling is an individual contributor role within the Risk & Regulatory Compliance job function, aligned to Enterprise Risk Management. The role is responsible for developing, enhancing, and maintaining quantitative risk models and analytics that support the firm’s enterprise-wide risk framework, including capital, liquidity, credit, market, operational, and strategic risk assessments, as applicable.

The role contributes to independent risk measurement, stress testing, and scenario analysis that inform senior management decision-making and support regulatory and internal risk governance requirements. The incumbent applies advanced quantitative techniques to design and implement robust, well-documented models, ensuring that model assumptions, methodologies, data, and outputs adhere to BNY’s model risk standards and Enterprise Risk Management policies.

The role supports the BNY strategic pillars by enabling stronger risk-aware growth and operational resilience through high-quality, transparent risk metrics, and supports BNY’s principles by demonstrating accountability, good judgement, and client focus in the development and communication of risk analyses.

The Senior Specialist, Risk Modeling collaborates with partners across Enterprise Risk Management, Model Risk Management, Finance, lines of business, and Engineering to ensure models are conceptually sound, implemented effectively, and continuously improved over time. The role emphasizes clear documentation, reproducible analytics, and the ability to articulate complex quantitative concepts in a concise and accessible way to risk, business, and control stakeholders.

PRIMARY RESPONSIBILITIES

• Develops and enhances quantitative risk models and analytical tools (e.g., for capital, liquidity, credit, market, or enterprise-wide risk measures) by applying advanced statistical, econometric, and mathematical techniques and ensuring alignment with Enterprise Risk Management frameworks and risk policies.
• Performs rigorous model performance monitoring, sensitivity analysis, and back-testing by designing and executing quantitative tests, analyzing outcomes, and recommending model refinements to maintain accuracy, stability, and regulatory compliance.
• Prepares high-quality model documentation, including model design, data sources, assumptions, limitations, and test results, by following established model governance and Enterprise Risk Management standards to support internal approvals, audits, and regulatory reviews.
• Contributes to enterprise stress testing and scenario analysis by translating macroeconomic and risk scenarios into model inputs, running simulations, and synthesizing results into clear risk insights for senior risk leaders and business partners.
• Partners with Model Risk Management, business, Finance, and Engineering teams by gathering requirements, explaining model methodologies and limitations, and responding to challenge and feedback to promote transparent, well-understood risk measures across the firm.
• Identifies opportunities to automate and industrialize recurring analytics and reporting by leveraging programming languages and data tools to build efficient, scalable processes that improve timeliness, control, and consistency of risk information.

EDUCATION/QUALIFICATIONS

Bachelor’s degree in a quantitative discipline such as mathematics, statistics, econometrics, engineering, physics, computer science, finance, or related field required. Advanced degree (Master’s or PhD) in a quantitative discipline preferred. Strong background in quantitative methods, numerical analysis, and statistical or econometric modeling. Proficiency in one or more programming languages or analytical tools (e.g., Python, R, SAS, MATLAB, or similar) and experience working with large datasets preferred.

EXPERIENCE

Typically 4-6 years of experience

SKILLS

Strong quantitative modeling and analytical skills, including experience with statistical/econometric techniques, validation concepts, and performance measurement for risk models. Proficiency in programming and data manipulation, with the ability to design, implement, and automate robust, reproducible quantitative analyses. Effective communication and documentation skills, with the ability to clearly explain model design, assumptions, limitations, and results to both technical and non-technical stakeholders.