Quantitative Finance Analyst

Bank of America•Charlotte, NC

1d•Onsite

About The Position

The Quantitative Finance Analyst conducts quantitative analytics and modeling projects for specific business units or risk types, including developing models, analytic processes, and systems approaches; preparing technical documentation; and working with Technology staff on systems to run models. The role requires broad knowledge of financial markets and products. Within Global Risk Management’s Data Strategy & Management function, the analyst contributes to designing and prototyping data-centric AI solutions, data platforms, architecture, and core algorithms. The role evaluates data and AI tools, conducts proofs of concept and pilot projects, recommends solutions, and develops remediation plans. It also supports risk modelers with data pipelines and API services, helps operationalize data governance, and advances data and AI capabilities across risk business lines. Bank of America describes its culture as inclusive and focused on Responsible Growth, collaboration, career development, and teammate wellness. The company is committed to an in-office culture, with flexibility based on role responsibilities and business needs. This position is based in Jersey City, New Jersey, and is a first-shift role for 40 hours per week.

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, including LangGraph, LangChain, embeddings, chunking, RAG, vector stores, and graphical context processing.
Track record 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 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 experience with data technologies and computing frameworks, including Python, Spark, Airflow, JavaScript, and SQL.
Ability to research data technologies, architect solutions to 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 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 scenario drivers.
Support planning and prioritization of quantitative work in alignment with the bank’s overall strategy.
Identify continuous improvements by reviewing model development and validation approval decisions, providing feedback on technical documentation, and challenging development and validation work.
Support model development and model risk management in relevant 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.
Help design and prototype data-centric AI features, data environments, architectures, and algorithms for data and AI-powered solutions.
Evaluate data and AI tools, conduct proofs of concept and pilot projects, recommend solutions, and develop 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.
Prototype UI/UX execution for data-centric AI solutions by specifying interfaces, approval flows, and response formats, and guiding Figma design and Angular work for stakeholder validation.
Work with business stakeholders to 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 the design and maintenance of AI skills.
Design semantic data intelligence agents for data contracts, lineage traceability, quality controls, and monitoring; measure maturity and drive remediation programs for risk information capabilities.
Promote and design data and AI solutions for risk lines of business and help define data strategy and information architecture goals, principles, and procedures.
Manage linkages between information capabilities and technical assets to understand end-to-end change impacts.
Advance adoption of 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.