Bank of America

Position Title/Short Description

Full Text of JOE Listing:

Overview of Global Risk Analytics:

Bank of America Merrill Lynch has an opportunity for a Quantitative Finance Analyst 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 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 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 reason-ability

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

Position Overview:

Responsible for independently conducting quantitative analytics and modeling projects. Responsible for developing new models, analytic processes or systems approaches. Creates documentation for all activities and works with Technology staff in design of any system to run models developed. Incumbents possess excellent quantitative/analytic skills and a broad knowledge of financial markets and products..

Required Education, Skills, and Experience:

Graduate degree in quantitative discipline (e.g. Mathematics, Economics, Engineering, Finance, Physics)
2+ years of experience in model development, statistical work, data analytics or quantitative research, or PhD
Strong Programming skills e.g. R, Python, SAS, SQL, R or other languages
Strong analytical and problem-solving skills
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
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
Understanding of various statistical modelling techniques
Research publications in prominent model related venues; e.g., conferences, journals

Desired Skills and Experience:

Experience with complex data architecture, including modeling and data science tools and libraries, data warehouses, and machine learning
Knowledge of predictive modeling, statistical sampling, optimization, machine learning and artificial intelligence techniques
Ability to extract, analyze, and merge data from disparate systems, and perform deep analysis
Experience designing, developing, and applying scalable Machine Learning and Artificial Intelligence solutions
Experience with data analytics tools (e.g., Alteryx, Tableau)
Experience with LaTeX
Sees the broader picture and is able to identify new methods for doing things
Broad understanding of financial markets and products
Experience with statistical data analysis and experimental design
Ability to produce high-quality technical documentation
Model documentation reviews
Strong understanding in one or more specialized areas; e.g., deep learning (DL), Reinforcement learning (RL), planning, information representation and retrieval, graphs, multiagent systems (MAS), natural language processing (NLP), regression analysis, time series forecasting, financial modelling
Experience with Machine Learning platforms such as Tensorflow/Keras, PyTorch.