Quantitative Finance Analyst

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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 Analytics (GRA) and Enterprise Independent Testing (EIT) are sub-lines of business within Global Risk Management (GRM). Collectively, they are responsible for developing a consistent and coherent set of models, analytical tools, and tests for effective risk and capital measurement, management and reporting across Bank of America. GRA and EIT partner with the Lines of Business and Enterprise functions to ensure the capabilities it builds address both internal and regulatory requirements, and are responsive to the changing nature of portfolios, economic conditions, and emerging risks. In executing its activities, GRA and EIT drive innovation, process improvement and automation.

As a part of Global Risk Analytics, Global Financial Crimes Modeling and Analytics is responsible for enterprise-wide financial crime model development and implementation, ongoing performance monitoring and optimization, data usage, and research and development utilizing advanced analytical tools and systems. The Global Financial Crimes Modeling and Analytics team is made up of nine sub-teams:

• Modeling and Analytics Team are responsible for model inventory management, model development and enhancement, model tuning and optimization, model risk management, and model analysis and incident management:

• US AML Modeling and Analytics is responsible for development and maintenance of all US AML Feeder models as per acceptable model risk practices and defined performance parameters to meet firm's AML Risk Coverage, while maintaining operational viability.

• Non-US AML Modeling & Analytics is responsible for development and maintenance of Non-US AML Feeder models to address the regional regulatory guidelines while meeting the bank's AML Risk Coverage. Automates suspicious activity monitoring while optimizing the effectiveness and the efficiencies of our models for the detection of potential threats.

• Case Generation Modeling & Analytics is responsible for the EP model which consolidates and risk ranks alerts generated from US and Non-US AML detection models and promotes suspicious activity as cases for investigation.

• Economic Sanction and Screening Modeling & Analytics is responsible for models that detect and prohibit transactions made by individuals or entities that are listed on sanctions watch lists.

• Ongoing Monitoring Review, Management Information, Analysis and Below-the-Line/Threshold (BTL/BTT) Testing is responsible for periodically substantiating the ongoing fitness of financial crime models in accordance with a model's approved Ongoing Monitoring Plan ("OMP"). Ongoing Model Monitoring Reports ("OMRs") assess environmental changes, model limitations, assumptions, process verification and outcomes analysis for each model. OMRs summarize trends in key metrics and provide critical analysis of model performance with respect to metric thresholds; identify threshold breaches and document remediation plans. In addition, the ongoing monitoring process includes the inline monitoring activities performed between reporting cycles, the results of those activities, and any escalations during the period. The team is also responsible for management information design and implementation, investigations forecasting, and BTL/BTT framework design and oversight.

• Engineering, Data & Analytics is responsible for model development and testing platforms, model production and delivery, model data framework, key business elements, and specialized and complex analytics.

• -Research and Development is responsible for future thinking concepts, innovation coordination, tools and technique assessment, artificial intelligence oversight, and vendor assessment oversight.

• Program Management & Regulatory is responsible for overseeing cross-functional initiatives and providing project management support to deliver timely execution of GFCMA's book of work, strategic initiatives, and critical activities in support of regulatory and audit deliverables.

• Business Management & Control is responsible for Strategy, Governance Oversight and Control, Resource Management, Process Excellence, Issue Management and COO function.

As a Quantitative Finance Analyst on the Global Financial Crimes Modeling and Analytics team, your main responsibilities will involve performing more complex analysis and supporting development of Non-US AML model development.

Required Qualifications:

• Effectively creates a compelling story using data; Able to make recommendations and articulate conclusions supported by data

• Strong Programming skills e.g. R, Python, SAS, SQL, R or other languages

Desired Qualifications:

• Experience with complex data architecture, including modeling and data science tools and libraries, data warehouses, and machine learning

• Knowledge of predictive modeling, statistical sampling, optimization, machine learning and artificial intelligence techniques

• Ability to extract, analyze, and merge data from disparate systems, and perform deep analysis

• Experience designing, developing, and applying scalable Machine Learning and Artificial Intelligence solutions

• Experience with data analytics tools (e.g., Alteryx, Tableau)

• Demonstrated ability to drive action and sustain momentum to achieve results

• Experience with engineering complex, multifaceted processes that span across teams; Able to document process steps, inputs, outputs, requirements, identify gaps and improve workflow

• Sees the broader picture and can identify new methods for doing things

• Experience with LaTeX

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

Minimum Education Requirement: Master's degree in related field or 2+ years equivalent work experience

Shift:
1st shift (United States of America)

Hours Per Week:
40

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