Quantitative Finance Analyst

Bank of America

. Conduct end-to-end market risk stress testing, including scenario design, implementation, results consolidation, reporting, and analysis .

Posted 10/14/2026full-timeUnited StatesMid-LevelSenior💰 $89,800 - $155,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in data management and AI solutions, with a strong focus on risk reporting systems, data governance, and architecture. Proficient in developing and operationalizing data strategies, data pipelines, and AI-powered applications.

Highest-signal resume keywords

Data Management & GovernanceRisk Reporting SystemsPython ProgrammingAgile Delivery MethodologiesStakeholder Management

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data PipelinesData WarehousesAPI DesignStatistical AnalysisData ModelingMicroservicesGenAI PatternsData Quality FrameworksGraph ProcessingSQL

Soft Skills

Cross-Functional LeadershipExcellent Communication

Tools & Technologies

PythonSparkAirflowCollibraApache AtlasDelta LakeJSONAvroLangChainTigerGraph

Industry Keywords

Data StrategyData ContractsData MeshGovernance FrameworksBig Data Environments

Tech Stack

Tools & technologies

AirflowApacheJavaScriptMicroservicesNoSQLPythonSparkSQL

About the role

Key responsibilities & impact

Conduct end-to-end market risk stress testing, including scenario design, implementation, results consolidation, reporting, and analysis
Support planning and prioritization of quantitative work aligned with bank strategy
Identify continuous improvements through reviews of model development and validation decisions and documentation
Support model development and model risk management within assigned focus areas
Provide methodological, analytical, and technical guidance to challenge and influence development and validation projects
Communicate submission and validation outcomes to model stakeholders and senior management
Perform statistical analysis on large datasets using qualitative and quantitative approaches
Design features to simplify and optimize the data environment through data-centric AI
Contribute to architecture, prototyping, and core algorithms for data and AI-powered solutions
Evaluate data and AI tools and conduct proof-of-concepts and pilot projects
Develop remediation plans to implement recommended solutions
Define target-state architectures and design artifacts for data platforms, reporting systems, and governance workflows
Prototype UI/UX execution for data-centric AI solutions, including interfaces, approval flows, and response formats
Rapidly prototype, customize, and productionize GenAI/LLM use cases such as RAG, agentic workflows, and risk analytics copilots
Build data pipelines and API services using Python, Spark, SQL, relational, NoSQL, and graph data stores
Support prompt engineering and AI skills design and maintenance
Operationalize governance through semantic data intelligence agents for data contracts, lineage, quality controls, and monitoring
Design new data and AI solutions for risk lines of business
Define data strategy and information architecture mission, goals, principles, and procedures
Manage linkages from information capabilities to operational and analytical technical assets
Champion data products, knowledge/property graphs, LLM-based RAG systems, agentic AI, and GenAI-assisted stewardship

Requirements

What you’ll need

Bachelor’s degree in computer science, engineering, Data Science, or Analytics and 4+ years of experience in data & AI platform/solutions and data management; or a Master’s degree and 2+ years’ 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
Proven 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 and SAFe
Excellent written, verbal, and presentation communication skills
Strong experience designing and developing data & AI solutions, data management & governance products, and data/reporting platforms
Expertise in complex design patterns, microservices, API design, data warehouses, data lakes, and data pipelines
Ability to drive data strategy and understanding of data mesh, data contracts, and integration fabric
Experience with relational and NoSQL data stores and big data environments
Hands-on expertise with Python, Spark, Airflow, JavaScript, and SQL
Ability to research new data technologies, architect data solutions, and validate designs through hands-on prototyping
Experience with data modeling for complex data pipelines, data lakes, and data platforms
Strong experience with data management platforms such as Collibra or open-source frameworks including Apache Atlas, Amundsen, DataHub, and Marquez
Working knowledge of Apache Iceberg, Hudi, Delta Lake, Parquet, JSON, and Avro
Experience with graph processing and storage technologies; working knowledge of at least one graph store such as TigerGraph
Exposure to Linked Data, Open Data, and GenAI-based data solutions is a plus

Benefits

Comp & perks

In-office culture supporting collaboration, engagement, and career development
Opportunities to learn, grow, and make an impact
Annual discretionary incentive plan eligibility
Industry-leading benefits
Paid time off
Resources and support for employees