Vista previa de la oferta
Quantitative Finance Analyst
mid · Data Science & Analytics
Job Description:
At Bank of America, we are guided by a common purpose to help make financial lives better through the power of every connection. We do this by driving Responsible Growth and delivering for our clients, teammates, communities and shareholders every day.
Being a Great Place to Work and providing a culture of caring is core to how we drive Responsible Growth. We are intentional about fostering an inclusive workplace where every teammate has the opportunity to succeed, build a career and contribute to our shared success. This includes attracting and developing exceptional talent, recognizing and rewarding performance, and supporting our teammates’ physical, emotional, and financial wellness through affordable, competitive and flexible benefits.
We value the unique perspectives individuals bring from all backgrounds and career paths - whether shaped by military service, community college education, or a wide range of work and life experiences. These journeys foster resilience, leadership and innovation, strengthening our workforce and positively impact the communities we serve.
Bank of America is committed to an in-office culture that supports collaboration, engagement, and career development. Our approach includes clear in-office expectations, while providing an appropriate level of flexibility based on role-specific responsibilities and business needs.
At Bank of America, you can build a successful career with opportunities to learn, grow, and make an impact. Join us!
Job Description:
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.
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
Global Risk Management (GRM) leads bank-wide initiatives for management of all aspects of risk, including strategic, market, credit, compliance, liquidity, operational, model and reputational risk matters to support sustainable, profitable corporate growth.
In a data driven economy, strategic data asset management is foundational to add to the enterprise value. Within GRM, we have established the Data Strategy & Management (DSM) function. A key pillar of this function is a strong data management, data architecture and data platforms foundation.
Job Description:
Under the GRM DSM Executive’s leadership, the Quantitative Finance Analyst will help design features to simplify and optimize the data environment through data centric AI, and be accountable for contributing to the architecture & prototyping along with core alogrithms for various data and AI powered solutions. Additionally, the analyst will also help evaluate data & AI tools and conduct proof of concepts & pilot projects to arrive at recommended solutions and develop remediation plans to implement those solutions.
Responsibilities:
Demonstrates knowledge of data & AI solutions, data platforms, context engineering, agentic AI workflows, data management & model governance practices and standards.
Define target-state architectures and design artifacts (data models, API specifications, integration patterns) for data platforms, reporting systems and governance workflows across risk domains.
Prototype UI/UX execution for data centric AI solutions by specifying user interfaces, approval flows, and response formats, guiding Figma design and Angular for stakeholder validation.
Design forward-deployed AI solutions (FDE model) by working closely with business stakeholders to rapidly prototype, customize, and productionize GenAI/LLM use cases (e.g., RAG, agentic workflows, risk analytics copilots).
Assist quantitative modelers by building data pipelines, API services for protyping data & AI products using python, Spark, SQL, Relational, NoSQL and Graph data stores.Assist with prompt engineering, AI skills design and maintainance.
Operationalize governance by solutioning Semantic Data Intelliegnce agents for data contracts, lineage traceability, quality controls and monitoring; measure maturity and drive remediation programs for GRM information capabilities.
Evangelize and design new data & AI solutions and capabilities to support risk lines of businesses.
Participates in efforts to define the mission, goals, critical success factors, principles, and procedures for data strategy and information architecture.
Understands the end-to-end change impact by managing linkages from information capabilities to technical assets (operational + analytical)
Champion innovation and adoption of modern paradigms—data products, knowledge/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.
Required Skills:
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 if Master’s degree, 2+ years’ experience.
Strong experience working 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, lang graph, lang chain, embedding, chunking, RAG, vector stores as well as graphical context processing.
Proven 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 (e.g. Scrum, SAFe).
Excellent communication skills (written, verbal and presentation) with the ability to translate regulatory language into actionable technical requirements.
Strong experience driving the design and development of data & AI solutions, data management & governance products as well as data and reporting platforms.
Expertise in architecting complex design patterns, microservices, API design, data warehouse and data lakes and data pipelines.
Ability to drive data strategy and deep understanding of industry paradigms such as data mesh, data contracts, integration fabric etc.
Experience with relational and NoSQL data stores and big data environments.
Hands-on expertise with data technologies and computing frameworks including but not limited to, Python, Spark, Airflow, Javascript and SQL.
Ability to research new data technologies, architect novel data solutions for business problems and prove design approach through hands-on prototyping.
Experience with data modeling for complex data pipelines, data lake and data platforms.
Strong experience with data management platform such as Collibra (preferred) or understanding of open-source frameworks such as Apache Atlas, Amundsen, Datahub, Marquez etc.
Exceptional communication skills and the ability to communicate effectively at all levels of the organization; this includes written and verbal communications as well as visualizations.
Desired Skills:
Working knowledge of data storage layers / formats such as Apache Iceberg, Hudi, Delta Lake etc. as well as Parquet, JSON and Avro.
Experience with Graph processing and storage technologies such as Knowledge Graphs / Property Graphs with working knowledge of at-least one graph store such as TigerGraph (preferred).
Exposure to Linked Data / Open Data, and GenAI based data solutions is a plus.
Skills:
Critical Thinking
Quantitative Development
Risk Analytics
Risk Modeling
Technical Documentation
Adaptability
Collaboration
Problem Solving
Risk Management
Test Engineering
Data Modeling
Data and Trend Analysis
Process Performance Measurement
Research
Written Communications
Shift:
1st shift (United States of America)Hours Per Week:
40