Quantitative Finance Analyst

Bank of America • Jersey City, NJ

9d • $89,800 - $155,000 • Onsite

About The Position

The Quantitative Finance Analyst will support Bank of America’s Global Risk Management Data Strategy & Management function by helping design and prototype data-centric AI solutions and optimize the data environment. The role contributes to architecture and core algorithms for data and AI solutions, evaluates tools, conducts proofs of concept and pilot projects, and develops recommendations and remediation plans for implementation. The position also supports quantitative modelers through data pipelines and API services, and works with business stakeholders to prototype, customize, and productionize generative AI and large language model use cases.

The role contributes to data strategy, information architecture, data management, governance, reporting platforms, and risk analytics across risk domains. It involves defining target-state architectures and design artifacts; prototyping user experiences; applying AI patterns such as retrieval-augmented generation and agentic workflows; and operationalizing data governance through controls for data contracts, lineage, quality, and monitoring. The analyst works with stakeholders and technical teams to advance adoption of modern data and AI capabilities, improve discoverability and impact analysis, and support timely insights across the risk ecosystem. The broader job description also identifies quantitative analytics and modeling, market-risk stress testing, statistical analysis, model risk management, technical documentation, and collaboration with Technology staff as responsibilities of the role.

Requirements

• Bachelor’s degree in computer science, engineering, data science, or analytics and at least 4 years of experience in data and AI platforms or solutions and data management; alternatively, a master’s degree and at least 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 generative AI 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 driving the design and development of 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 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 data technologies and computing frameworks, including Python, Spark, Airflow, JavaScript, and SQL.
• Ability to research new data technologies, architect novel data 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 through written and verbal communication 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 such as knowledge graphs or property graphs, and working knowledge of at least one graph store; TigerGraph is preferred.
• Exposure to Linked Data, Open Data, and generative-AI-based data solutions.

Responsibilities

• Conduct end-to-end market risk stress testing, including scenario design and implementation, results consolidation, internal and external reporting, and analysis of key 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 challenging model development and validation work.
• Support model development and model risk management in assigned focus areas to meet business requirements and the enterprise’s 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.
• Help design features to simplify and optimize the data environment through data-centric AI, contributing to architecture, prototyping, and core algorithms for data and AI solutions.
• Evaluate data and AI tools, conduct proofs of concept and pilot projects, recommend solutions, and develop remediation plans for implementation.
• 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 user interfaces, approval flows, and response formats for data-centric AI solutions; guide Figma design and Angular execution for stakeholder validation.
• Work with business stakeholders to rapidly prototype, customize, and productionize generative AI and LLM use cases, including RAG, agentic workflows, and risk analytics copilots.
• Build data pipelines and API services for data and AI product prototypes using Python, Spark, SQL, and relational, NoSQL, and graph data stores; assist with prompt engineering and AI skills design and maintenance.
• Operationalize governance by designing semantic data intelligence agents for data contracts, lineage traceability, quality controls, and monitoring; measure maturity and drive remediation programs for GRM information capabilities.
• Evangelize and design data and AI solutions and capabilities to support risk lines of business.
• Participate in 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 impact.
• Champion 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

• Industry-leading benefits and access to paid time off.
• Resources and support to help employees make an impact and contribute to business and community growth.
• Annual discretionary incentive plan, with awards based on individual performance and behavior, line-of-business or group performance, and overall company success.

What This Job Offers

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