Risk Methodology Modeler - Vice President

Deutsche Bank•New York, NY

22h•$125,000 - $222,500•Hybrid

About The Position

The Group Strategic Analytics (GSA) - Risk Methodology Team is responsible for cutting-edge modeling methods and technology that transform the way that we work. We are seeking a Risk Methodology Modeler to support the state-of-the-art model development practices for balance sheet and revenue projections, you will be involved in developing models such as pre-provision net revenue (PPNR) models required for various business functions including stress testing, capital and strategic planning. The Risk Methodology specialist will be responsible for the end-to-end model development process, which includes model methodology design, documentation, the on-going monitoring of the models and various model maintenance activities. Model maintenance covers resolving validation findings, periodic model recalibrations, self and business identified model enhancements, sensitivity and attribution analysis for stress testing cycles. You will actively communicate and engage with the stakeholders at various stages of the model development, implementation and execution process, as Deutsche Bank strengthens best-in-class capital management practices, you’ll bring curiosity and critical thinking to help advance our modeling methodologies and ways of working. What We Offer You A diverse and inclusive environment that embraces change, innovation, and collaboration A hybrid working model, allowing for in-office / work from home flexibility, generous vacation, personal and volunteer days Employee Resource Groups support an inclusive workplace for everyone and promote community engagement Competitive compensation packages including health and wellbeing benefits, retirement savings plans, parental leave, and family building benefits Educational resources, matching gifts and volunteer programs

Requirements

Demonstrated quantitative skills, including hands-on experience with time series analysis and forecasting models (BPPNR experience preferred)
Working knowledge of banking and trading products, and familiarity with stress testing and capital planning frameworks
Proficiency in Python and R for data analysis, statistical modeling, and production-quality coding
Strong written and spoken communication skills, with the ability to present complex analyses to technical and non-technical stakeholders
Master’s degree (or higher) in a quantitative field such as Mathematics, Finance, Economics, Statistics, or Computer Science

Nice To Haves

A self-starter with intellectual curiosity and strong critical thinking skills
Experience applying AI tools and machine learning techniques in analytics or modeling processes
A continuous-improvement mindset, with a focus on improving model performance and operational excellence
Familiarity with asset-liability management (ALM) principles
Familiarity with accounting frameworks (e.g., IFRS, US GAAP) and hedge accounting concepts

Responsibilities

Develop, document, implement, and maintain balance sheet and revenue (BPPNR) forecasting models and analytical tools across Investment, Corporate, and Private Banking
Identify key risk drivers and model relationships between portfolio performance and macroeconomic variables using statistical techniques and data analytics
Partner with model owners and users to understand business expectations and upcoming changes, and incorporate feedback in a timely manner
Support model reviews with Model Risk Management (MoRM) and Group Audit, including ongoing performance monitoring and remediation of findings
Support stress testing cycles end-to-end, from input collection and quality checks through sensitivity analysis, risk driver attribution, and results explanation
Present model methodologies and projection results to senior management and regulatory stakeholders as needed
Build trusted partnerships with business owners (Trading and Banking) and key model users (Finance, Treasury, Capital Management) to stay on top of strategic planning and priorities
Own follow-ups on Model Risk Management and Group Audit items, coordinating responses and driving remediation to closure
Collaborate with data providers and platform teams to automate data ingestion, strengthen data quality controls, and ensure reliable, accurate model outputs