Sr Quantitative Financial Analyst

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Job Description:
This job is responsible for conducting quantitative analytics and complex modeling projects for specific business units or risk types. Key responsibilities include leading the development of new models, analytic processes, or system approaches, creating technical documentation for related activities, and working with Technology staff in the design of systems to run models developed. Job expectations may include the ability to influence strategic direction, as well as develop tactical plans.

Responsibilities:

• Performs end-to-end market risk stress testing including scenario design, scenario implementation, results consolidation, internal and external reporting, and analyzes stress scenario results to better understand key drivers

• Leads the planning related to setting quantitative work priorities in line with the bank’s overall strategy and prioritization

• Identifies continuous improvements through reviews of approval decisions on relevant model development or model validation tasks, critical feedback on technical documentation, and effective challenges on model development/validation

• Maintains and provides oversight of model development and model risk management in respective focus areas to support business requirements and the enterprise's risk appetite

• Leads and provides methodological, analytical, and technical guidance to effectively challenge and influence the strategic direction and tactical approaches of development/validation projects and identify areas of potential risk

• Works closely with model stakeholders and senior management with regard to communication of submission and validation outcomes

• Performs statistical analysis on large datasets and interprets results using both qualitative and quantitative approaches

Overview of Global Risk Analytics:

Bank of America Merrill Lynch has an opportunity for a Senior Quantitative Finance Analyst / Quantitative Finance Manager within our Global Risk Analytics (GRA) function. GRA is a sub-line of business within Global Risk Management (GRM).  GRA is responsible for developing a consistent and coherent set of models and analytical tools for effective risk and capital measurement, management and reporting across Bank of America. GRA partners with the Lines of Business and Enterprise functions to ensure that its models and analytics address both internal and regulatory requirements, such as quarterly Enterprise Stress Testing (EST), the annual Comprehensive Capital Analysis and Review (CCAR), and the Current Expected Credit Losses (CECL) accounting standard.  GRA models follow an iterative and ongoing development life cycle, as the bank responds to the changing nature of portfolios, economic conditions and emerging risks.  In addition to model development, GRA conducts model implementation, data management, model execution and analysis, forecast administration, and model performance monitoring. GRA drives innovation, process improvement and automation across all these activities. 

Overview of Global Risk Analytics Team:

The Consumer Model Development & Operations (CMDO) team is part of Global Risk Analytics. It provides quantitative solutions to enable effective risk and capital management across the Retail and Global Wealth & Investments Management (GWIM) lines of business. The team places strong emphasis on delivering world class quantitative solutions for Front Line Unit (FLU) model owners and stakeholders through a disciplined and iterative development process. The team has responsibilities across a number of areas:

Overview of the Role:

The Consumer Model Development & Operations (CMDO) team is part of Global Risk Analytics. It provides quantitative solutions to enable effective risk and capital management across the Retail and Global Wealth & Investments Management (GWIM) lines of business.

The team places strong emphasis on delivering world class quantitative solutions for Front Line Unit (FLU) model owners and stakeholders through a disciplined and iterative development process. The team has responsibilities across a number of areas:

• Quantitative Modeling – Develop and maintain risk and capital Models and Model Systems across Retail and GWIM product lines. Models and Model Systems provide insight into many risk areas, including valuation modeling of residential real estate, loan default, exposure at default (EAD), loss given default (LGD), delinquency, prepayment, balances, pricing, risk appetite, revenues and cash flows.

• Quantitative Development – Architect, implement, maintain, improve and integrate quantitative solutions on strategic GRA platforms. Outputs include GRA libraries that perform consumer risk model calculations, analytical tools, processes and documentation. Partner in defining, adopting, and executing GRA’s technical strategy.

• Risk and Capital Management Capabilities – Build best in class quantitative solutions that enable the Retail and GWIM lines of business to effectively manage risk and capital, through the application of the disciplined BAU development process that includes extensive interaction with the FLU model owners and stakeholders throughout the quantitative lifecycle.

• Infrastructure – Partner in driving forward the infrastructure to support the goals of GRA through code efficiencies, and expansion of quantitative capabilities to better leverage infrastructure and computational resources.

• Documentation – Deliver concise, quantitative documentation to inform stakeholders, meet policy requirements, and enable successful engagement in regulatory exams (e.g., CCAR, CECL) via automated, modularized, and standardized documentation and presentations.

Qualified candidates must be able to work independently to provide sound economic reasoning, statistical analysis and deliver high quality modeling insights as well as modeling documentation. The ideal candidate is self-directed, collaborative, analytical, and proactive in execution and problem resolution. Specific tasks include:

• Set priorities related to quantitative modeling in line with the bank’s overall strategy and prioritization.

• Develop and design best in class models to satisfy stakeholder requirements.

• Identifies continuous improvement through reviews and ongoing monitoring of models, and effective challenges on model development and validation.

• Work closely with Technology Team to support model execution.

• Collaboration with Enterprise Model Risk Management to support model validations, and quickly and efficiently resolve outstanding issues.

• Create sophisticated, value-added analytic systems that support business operations, risk management, operational excellence, regulatory compliance, and research.

• Support business units and acting as a subject matter expert on specified quantitative modeling techniques, as well as oversee model performance, model risk and model governance on critical model portfolios.

• Work closely with model stakeholders and senior management with regard to communication of submission and validation outcomes.

Required Qualifications: Successful candidates will have a Master or PhD degree in Mathematics, Economics, Statistics, or similar discipline, and a minimum of 5 years relevant experience in statistics, data science, econometrics, and other quantitative analysis.

Successful candidates will possess the following skills:

• First-hand experience in large data analysis, statistical model estimation, implementation, and testing

• Ability to work in a large, complex organization, and influence various stakeholders and partners

• Strong team player able to seamlessly transition between contributing individually and collaborating on team projects; Understands that individual actions may require input from manager or peers; Knows when to include others.

• Strong communication skills and ability to effectively communicate quantitative topics to technical and non-technical audiences.

• Strong programming skills in SQL, Python, R

• Strong analytical and problem-solving skills

• Strong ownership and accountability for delivering high quality work, able to prioritize effectively, adapt, and meet strict deadlines

• Strong written, verbal, presentation creation and delivery skills

Desired Qualifications: The ideal candidate will possess the following skills and experience:

• Knowledge of financial services industry, consumer credit and products, real estate data and market, and related regulations

• Experience with HDFS, HIVE, and Spark

• Experience with CCAR and CECL

• Ability to apply CI/CD tools (e.g.,, Git, JIRA, Confluence, Pytest, Jenkins, and SonarQube) in model development process

• Experience implementing process improvements and automation.

• Managerial experience

• Data visualizations in Tableau