JOB DESCRIPTION:

At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.
Being a Great Place to Work is core to how we drive Responsible Growth. This includes our commitment to being a diverse and inclusive workplace, attracting and developing exceptional talent, supporting our teammates’ physical, emotional, and financial wellness, recognizing and rewarding performance, and how we make an impact in the communities we serve.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!

REQUIREMENTS:

• Master’s degree or equivalent in Finance, Statistics, Mathematics, or related: and
• 2 years of experience in the job offered or a related Quantitative occupation.
• Must include 2 years of experience in each of the following:
• Analyzing and evaluating large and complex economic and financial datasets with analytical tools of Python, SQL and R;
• Using big data technologies of SQL to handle large volumes of data produced by complex financial forecasting models;
• Implementing advanced high-performance Python code for large-scale distributed computing environments in Apache Spark and Hadoop/HIVE to develop scalable, reusable analytical and quantitative models; and,
• Utilizing current modeling and data science principals including time-series analysis and machine learning to develop loan loss modelling frameworks.
  If interested apply online at www.bankofamerica.com/careers or email your resume to bofajobs@bofa.com and reference the job title of the role and requisition number.
  EMPLOYER: Bank of America N.A.

• Participate in research development and implementation of credit risk concentration models.
• Perform econometric analyses to support methodology development.
• Conduct on-demand analyses of model output and changes.
• Develop a practical understanding of credit risk trends (financial performance of counterparties), concentrations (industry country and single name) analyzing macroeconomic indicators, and ability to recommend key credit portfolio decisions to be shared at senior management level and Board level discussions.
• Collaborate with global teams to provide analytical support for leadership decision making.
• Perform financial modelling, analysis and data collection, as well as the interpretation of market and portfolio information, statistics, and pricing.
• Analyze and evaluate large and complex economic and financial datasets with analytical tools of Python, SQL and R.
• Use big data technologies of SQL to handle large volumes of data produced by complex financial forecasting models.
• Implement advanced high performance Python code for large-scale distributed computing environments in Apache Spark and Hadoop/HIVE to develop scalable, reusable analytical and quantitative models.
• Utilize current modeling and data science principals including time-series analysis and machine learning to develop loan loss modelling frameworks.
• Remote work may be permitted within a commutable distance from the worksite.