Quantitative Finance Analyst

Bank of America • Charlotte, NC

1d • Onsite

About The Position

At Bank of America, the Quantitative Finance Analyst works within Global Risk Management’s Data Strategy & Management function. The role contributes to quantitative analytics and modeling projects, including model development, analytical processes, systems approaches, technical documentation, and collaboration with Technology teams to design systems that run models. It also supports market-risk stress testing and statistical analysis of large datasets, while communicating with model stakeholders and senior management.

The analyst helps design and optimize the bank’s data environment through data-centric AI, contributing to architecture, prototypes, and core algorithms for data- and AI-powered solutions. The role evaluates tools and develops proofs of concept and pilot projects, then recommends solutions and remediation plans. Work includes defining architectures and design artifacts, prototyping interfaces and approval flows, and rapidly prototyping and productionizing AI use cases with business stakeholders. The position also supports data pipelines and API services, prompt and AI-skill design, governance capabilities, and adoption of modern data and AI approaches across risk businesses.

Requirements

• Bachelor’s degree in computer science, engineering, data science, or analytics and 4+ years of experience in data and AI platforms or solutions and data management; alternatively, a master’s degree and 2+ years of experience.
• Strong experience with risk reporting systems, data warehouses, reporting tools, and governance frameworks.
• Familiarity with data quality frameworks, metadata management, data lineage tools, and control monitoring.
• Working knowledge of AI and GenAI patterns, LangGraph, LangChain, embeddings, chunking, RAG, vector stores, and graphical context processing.
• Track record of defining and delivering product roadmaps for complex data management or reporting platforms.
• Strong stakeholder management and cross-functional leadership skills.
• Proficiency in Agile delivery methodologies such as Scrum or SAFe.
• Excellent written, verbal, and presentation communication skills, including the ability to translate regulatory language into actionable technical requirements.
• Strong experience designing and developing data and AI solutions, data management and governance products, and data and reporting platforms.
• Expertise in complex design patterns, microservices, API design, data warehouses and data lakes, and data pipelines.
• Ability to drive data strategy and understanding of industry paradigms such as data mesh, data contracts, and integration fabric.
• Experience with relational and NoSQL data stores and big data environments.
• Hands-on expertise with technologies and computing frameworks including Python, Spark, Airflow, JavaScript, and SQL.
• Ability to research data technologies, architect solutions for business problems, and validate design approaches through hands-on prototyping.
• Experience with data modeling for complex data pipelines, data lakes, and data platforms.
• Strong experience with a data management platform such as Collibra, or understanding of open-source frameworks such as Apache Atlas, Amundsen, DataHub, or Marquez.
• Ability to communicate effectively at all organizational levels, including through written and verbal communications and visualizations.

Nice To Haves

• Working knowledge of data storage layers and formats such as Apache Iceberg, Hudi, Delta Lake, Parquet, JSON, and Avro.
• Experience with graph processing and storage technologies, including knowledge or property graphs, and working knowledge of at least one graph store; TigerGraph is preferred.
• Exposure to Linked Data, Open Data, and GenAI-based data solutions.
• Collibra experience is preferred.

Responsibilities

• Conduct end-to-end market risk stress testing, including scenario design and implementation, results consolidation, internal and external reporting, and analysis of scenario drivers.
• Support planning and prioritization of quantitative work in alignment with the bank’s overall strategy.
• Identify continuous improvements by reviewing model development or validation approval decisions, providing feedback on technical documentation, and effectively challenging model development and validation work.
• Support model development and model risk management in assigned focus areas in line with business requirements and enterprise risk appetite.
• Provide methodological, analytical, and technical guidance to challenge and influence development and validation projects and identify potential risks.
• Communicate submission and validation outcomes to model stakeholders and senior management.
• Perform statistical analysis on large datasets and interpret results using qualitative and quantitative approaches.
• Contribute to the architecture and prototyping of data- and AI-powered solutions, including core algorithms.
• Evaluate data and AI tools; conduct proofs of concept and pilot projects; recommend solutions and develop implementation remediation plans.
• Define target-state architectures and design artifacts, including data models, API specifications, and integration patterns for data platforms, reporting systems, and governance workflows across risk domains.
• Specify UI/UX interfaces, approval flows, and response formats for data-centric AI solutions; guide Figma design and Angular implementation for stakeholder validation.
• Work with business stakeholders to prototype, customize, and productionize AI use cases such as RAG, agentic workflows, and risk analytics copilots.
• Build data pipelines and API services for data and AI product prototypes using Python, Spark, SQL, relational, NoSQL, and graph data stores.
• Assist with prompt engineering and the design and maintenance of AI skills.
• Develop semantic data intelligence agents for data contracts, lineage traceability, quality controls, and monitoring; measure maturity and drive remediation programs for GRM information capabilities.
• Design and promote data and AI capabilities for risk lines of business.
• Contribute to defining the mission, goals, critical success factors, principles, and procedures for data strategy and information architecture.
• Manage linkages between information capabilities and operational and analytical technical assets to understand end-to-end change impacts.
• Promote data products, knowledge and property graphs, LLM-based RAG systems, agentic AI, and GenAI-assisted stewardship to improve discoverability, impact analysis, and time-to-insight across the risk ecosystem.

Benefits

• Benefits eligibility, including access to paid time off and resources and support for employees.
• Eligible to participate in the annual discretionary incentive plan; awards are based on individual performance and behaviors, line of business or group performance and contributions, and overall company success.
• Affordable, competitive, and flexible benefits supporting physical, emotional, and financial wellness.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level