Vice President; Quantitative Finance Analyst

Bank of America•Chicago, IL

3d•Hybrid

About The Position

At Bank of America, this role conducts quantitative analytics and modeling projects for specific business units or risk types. The position develops models, analytic processes, and systems approaches; creates technical documentation; and works with Technology staff on systems used to run models. The role requires broad knowledge of financial markets and products. The analyst supports credit risk concentration modeling and methodology development through research, econometric analysis, and ongoing analysis and production support. The role examines counterparties, industry, country, and single-name concentrations, analyzes macroeconomic indicators, and provides insights for portfolio management and senior leadership decisions. It also uses large financial datasets and advanced analytics—including Python, SQL, R, Apache Spark, Hadoop/HIVE, time-series analysis, and machine learning—to develop scalable quantitative models and loan loss modeling frameworks. The role is based at Bank of America Plaza in Chicago, Illinois. Remote work may be permitted within a commutable distance from the worksite. The position is a first-shift role with 40 hours per week. Salary offers are determined based on experience, education, and skill set. The role is benefits eligible.

Requirements

Master’s degree or equivalent in Finance, Statistics, Mathematics, or a related field.
At least 2 years of experience in the job offered or a related quantitative occupation.
At least 2 years of experience building scalable, production-grade analytical frameworks using Python, Apache Spark, Hadoop/HIVE, SQL, Tableau, and Alteryx, including modular reusable model components and automated end-to-end data processing workflows.
At least 2 years of experience developing and deploying financial forecasting models, including monitoring routines, performance diagnostics, testing, and investigation of model outputs.
At least 2 years of experience performing time-series analysis, multivariate regression, and econometric modeling on large financial datasets to identify structural, cyclical, and trend-based risk dynamics.
At least 2 years of experience applying advanced statistical, econometric, and machine learning techniques to develop quantitative forecasting and risk measurement frameworks supporting enterprise-level portfolio and capital management processes.
At least 2 years of experience integrating and analyzing diverse financial and alternative datasets—including market activity, firm-level microeconomic indicators, sector performance metrics, consumer behavior trends, and macroeconomic variables—to support advanced quantitative risk assessment and concentration analysis.

Responsibilities

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.
Provide ongoing production support for models in use.
Develop a practical understanding of credit risk trends, including counterparties’ financial performance, and industry, country, and single-name concentrations.
Analyze macroeconomic indicators and recommend key credit portfolio decisions for senior management and Board-level discussions.
Collaborate with global teams to provide analytical support for leadership decision-making.
Explore large datasets and extract insights to support portfolio management decisions.
Perform financial modeling, analysis, and data collection, and interpret market and portfolio information, statistics, and pricing.
Analyze and evaluate large, complex economic and financial datasets using tools such as Python, SQL, and R.
Use big data technologies such as SQL to handle large volumes of data produced by complex financial forecasting models.
Implement high-performance Python code for large-scale distributed computing environments in Apache Spark and Hadoop/HIVE to develop scalable, reusable analytical and quantitative models.
Apply current modeling and data science principles, including time-series analysis and machine learning, to develop loan loss modeling frameworks.