Bank of America
Bank of America
Bank of America is a global financial institution operating across banking, finance, and fintech. Its services include personal banking, small-business support, wealth management, investment services, and access to capital markets for individuals, businesses, and institutions. The company also focuses on responsible growth through sustainable finance, diversity initiatives, and community development programs.

Quantitative Finance Analyst - Charlotte Onsite

Join Bank of America’s Global Risk Analytics team to develop consumer risk and capital models for regulated banking decisions. Apply Python, machine learning, statistical analysis, and quantitative modeling in an onsite Charlotte role.

Description

  • Lead quantitative analytics and modeling initiatives for assigned business units and risk areas.
  • Create new models, analytical processes, and systems strategies.
  • Prepare technical documentation for quantitative modeling activities.
  • Partner with Technology teams to design systems that operate developed models.
  • Execute end-to-end market risk stress testing, from scenario design and implementation through results consolidation, reporting, and analysis.
  • Help prioritize quantitative work in alignment with the bank’s broader strategy.
  • Identify ongoing improvements through model development and validation reviews.
  • Support model development and model risk management in line with business needs and enterprise risk appetite.
  • Provide methodological, analytical, and technical guidance to challenge and shape development and validation efforts.
  • Communicate submission and validation outcomes to model stakeholders and senior management.
  • Analyze large datasets statistically and interpret findings through qualitative and quantitative methods.
  • Develop and maintain risk and capital models and supporting systems across Retail and GWIM product lines.
  • Design, implement, maintain, enhance, and integrate quantitative solutions on strategic platforms.
  • Develop quantitative solutions supporting risk and capital management.
  • Enhance infrastructure, code performance, and computational resource utilization.
  • Produce quantitative documentation for stakeholders, policy requirements, and regulatory examinations, including CCAR and CECL.
  • Collaborate with senior modelers on model design and execution support.
  • Work with Enterprise Model Risk Management on model validations and issue remediation.
  • Act as a quantitative modeling subject matter expert for business units.
  • Oversee model performance, model risk, and governance for critical model portfolios.

Requirements

  • A master’s degree in a related discipline or equivalent professional experience.
  • At least two years of relevant experience in statistics, data science, machine learning, model development, or quantitative analysis.
  • Experience estimating, implementing, testing, evaluating, documenting, and analyzing statistical and machine learning models.
  • Advanced programming ability in Python, SQL, and related quantitative or data science libraries.
  • Experience handling large, complex datasets, including extraction, transformation, validation, feature engineering, and quality review with SQL-based tools.
  • Experience with HDFS, Hive, Spark, PySpark, and distributed data-processing environments.
  • Practical experience developing machine learning or AI models with Python frameworks such as scikit-learn, XGBoost, LightGBM, Random Forest, or comparable ensemble methods.
  • Experience applying model explainability and transparency methods, including SHAP, feature importance, partial dependence, or interpretable models.
  • Ability to connect quantitative findings with business implications for residential property valuation, collateral risk, mortgage or home equity decision support, and model governance.
  • Experience producing quantitative documentation and technical content using LaTeX or comparable tools.
  • Strong analytical and problem-solving ability, with the independence to work autonomously and seek guidance when appropriate.
  • Ability to present quantitative analyses, model outputs, and recommendations to technical and non-technical audiences.
  • Ability to work in an in-office environment on a United States first-shift schedule.

Benefits

  • Eligible for discretionary incentives and participation in the annual discretionary plan.
  • Eligible for employee benefits.
  • Paid time off.
  • Access to employee resources and support.
  • An in-office culture designed to encourage collaboration, engagement, and career development.
  • Opportunities to learn, grow, and contribute meaningfully.

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