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Overview of Global Risk Analytics
Bank of America has an opportunity for a Senior Quantitative Finance Analyst (B4) 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 of these activities.
Overview of the Role
The Senior Quantitative Financial Analyst interacts with a wide variety of stakeholders including risk managers, model developers, operations, technology, finance, and capital. The Analyst will identify, lead, and organize strategic change efforts across the team including new model deployment and analytical capability development.
As a Senior Quantitative Finance Analyst within Global Risk Analytics, your main responsibilities will involve:
• Applying quantitative methods to develop capabilities that meet line of
business, risk management and regulatory requirements
• Maintaining and continuously enhancing capabilities over time to respond to the changing nature of portfolios, economic conditions and emerging risks
• Understanding and executing activities that form the end-to-end model development and use life cycle
• Identifying requirements from the teams which improve the group’s ability to generate insights and understanding of portfolio risk, model accuracy, and forecast reasonability
• Articulating the overall holistic picture of model performance, with clear conclusions regarding accuracy and remediation areas as required
• Clearly documenting and effectively communicating quantitative methods as part of ongoing engagement with key stakeholders, including the lines of business, risk managers, model validation, technology
Required Education, Skills, and Experience
• Masters degree (e.g. Mathematics, Economics, Engineering, Finance, Physics, Data Science or other relatable degree)
• 7+ years of experience in model development, statistical work, data analytics or quantitative research, or PhD
• Ability to work in a large, complex organization, and influence various stakeholders and partners
• Strong communication skills and ability to effectively communicate quantitative topics to technical and non-technical audiences
• Effectively presents findings, data, and conclusions to influence senior leaders
• Demonstrated leadership skills; Ability to exert broad influence among peers
• Sees the broader picture and is able to identify new methods for doing things
• Strong Programming skills e.g. R, Python, SAS, SQL or other languages
• Strong analytical and problem-solving skills
• Experience using and developing cross-sectional models
• Experience implementing models into various production environments
Desired Skills and Experience
• Experience with complex data architecture, including modeling and data science tools and libraries, data warehouses, and machine learning
• Ability to extract, analyze, and merge data from disparate systems, and perform deep analysis
• Experience using data mining and other advanced analytical techniques to aggregate data for model development and/or to produce management reporting
• Experience designing, developing, and applying scalable Machine Learning and Artificial Intelligence solutions
• Experience with data analytics tools (e.g., Alteryx, Tableau)
• Experience building data architecture that is optimized for large dataset retrieval, analysis, storage, cleansing, and transformation
• Demonstrated ability to drive action and sustain momentum to achieve results
• Experience with engineering complex, multifaceted processes that span across teams; Able to document process steps, inputs, outputs, requirements, identify gaps and improve workflow
• Experience with LaTeX
1st shift (United States of America)
**Hours Per Week: **