Liquidity Reporting Quantitative Finance Analyst

Bank of America•Charlotte, NC

1d•Onsite

About The Position

This job is responsible for conducting quantitative analytics and modeling projects for specific business units or risk types. Key responsibilities include developing new models, analytic processes, or systems approaches, creating technical documentation for related activities, and working with Technology staff in the design of systems to run models developed. Job expectations include having a broad knowledge of financial markets and products.

Requirements

Proven ability to analyze and manipulate large datasets with strong attention to detail and data integrity.
Experience applying statistical or quantitative methods to real-world financial questions.
Fundamental understanding of liquidity risk metrics (e.g., cash flow forecasting, liquidity coverage, or stress testing), balance sheet structure, and financial market dynamics.
Familiarity with banking products and liquidity-related regulatory concepts.
Excellent organizational skills with the ability to manage multiple priorities, meet deadlines, and adapt in a time-sensitive environment without sacrificing quality.
Self-motivated and able to work independently with minimal supervision.
Strong written and verbal communication skills, including the ability to distill complex quantitative analyses into clear insights for technical and non-technical stakeholders.
Experience with formal presentations or reporting of analytical findings.
Demonstrated ability to collaborate effectively with cross-functional teams (e.g., technology, risk, finance) and proactively share knowledge.

Nice To Haves

Proficiency in Python programming for data analysis, statistical modeling, and automation
Strong knowledge of SQL and relational databases for complex data extraction and transformation tasks
Experience with data analytics and workflow tools such as Alteryx
Familiarity with data visualization platforms like Tableau to create intuitive dashboards and reports.
Experience developing or using quantitative models (particularly in risk or liquidity contexts), statistical analysis techniques (e.g., regression analysis, scenario simulation, Monte Carlo methods), or working with large-scale data frameworks.
Background in liquidity risk management or regulatory liquidity reporting (such as LCR, NSFR, FR 2052a, or other liquidity stress testing processes) is highly desirable.
Knowledge of balance sheet management and ALM (Asset-Liability Management) practices is a plus.
Master’s degree in a relevant quantitative discipline (Finance, Mathematics, Financial Engineering, Data Science, etc.) or professional certifications like FRM (Financial Risk Manager) or CFA are strongly preferred, indicating advanced analytical training and commitment to the field.

Responsibilities

Performs end-to-end market risk stress testing including scenario design, scenario implementation, results consolidation, internal and external reporting, and analyzes stress scenario results to better understand key drivers
Supports the planning related to setting quantitative work priorities in line with the bank’s overall strategy and prioritization
Identifies continuous improvements through reviews of approval decisions on relevant model development or model validation tasks, critical feedback on technical documentation, and effective challenges on model development/validation
Supports model development and model risk management in respective focus areas to support business requirements and the enterprise's risk appetite
Supports the methodological, analytical, and technical guidance to effectively challenge and influence the strategic direction and tactical approaches of development/validation projects and identify areas of potential risk
Works closely with model stakeholders and senior management with regard to communication of submission and validation outcomes
Performs statistical analysis on large datasets and interprets results using both qualitative and quantitative approaches
Work closely with Technology and data engineering teams to design and implement robust systems and infrastructure that run and scale the developed liquidity models and analytics
Participate in system testing, ensure model integration into enterprise platforms, and identify enhancements to data pipelines and analytical tools supporting liquidity risk management
Create and maintain comprehensive technical documentation for all liquidity stress methodologies, data processes, and assumptions