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. Responsible Growth is how we run our company and how we deliver for our clients, teammates, communities and shareholders every day.

One of the keys to driving Responsible Growth is being a great place to work for our teammates around the world. We’re devoted to being a diverse and inclusive workplace for everyone. We hire individuals with a broad range of backgrounds and experiences and invest heavily in our teammates and their families by offering competitive benefits to support their physical, emotional, and financial well-being.

Bank of America believes both in the importance of working together and offering flexibility to our employees. We use a multi-faceted approach for flexibility, depending on the various roles in our organization.

Working at Bank of America will give you a great career with opportunities to learn, grow and make an impact, along with the power to make a difference. Join us!

Job Description:
This job is responsible for conducting quantitative analytics and complex modeling projects for specific business units or risk types. Key responsibilities include leading the development of new models, analytic processes, or system approaches, creating technical documentation for related activities, and working with Technology staff in the design of systems to run models developed. Job expectations may include the ability to influence strategic direction, as well as develop tactical plans.

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

• Leads 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

• Maintains and provides oversight of model development and model risk management in respective focus areas to support business requirements and the enterprise's risk appetite

• Leads and provides 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

Minimum Education Requirement: Master’s degree in related field or equivalent work experience

• Review and validate fraud prevention and detection models for conceptual soundness and quantitative rigor to ensure they follow good modeling practices and Model Governance Policy, Guidelines, Testing Playbooks and Regulatory Requirements.
• Develop testing or alternative models by applying statistical or quantitative analysis, leveraging a variety of software programs, including R, SAS, Python, etc.
• Present model validation results in structured and comprehensive reports, including the Initial Assessment report (IAR), Model Validation Report (MVR), Required Action Item Assessment, and Ongoing Monitoring Report reviews.
• Partner closely and manage interactions with business, model developers, risk and audit partners across the model lifecycle and validation processes.

• Advanced quantitative degree (PhD or MS in statistics, math, physics, computer science, etc.) with 5+ years of hands-on model development or validation experience in fraud detection or related financial crime areas.
• A solid grasp of traditional statistical modeling techniques (e.g. Logistic regression) plus advanced AI/ML techniques (Gradient Boosting, Neural Networks, Random Forest, etc.)
• Expertise in model evaluation techniques such as rank ordering, ROC curve, confusion matrix, KS, cross-validation, feature importance, SHAP values, etc.
• Proficiency in Python, SAS, R, LaTEX, etc.
• Familiarity with regulatory requirements and guidelines related to risk model validation.
• Excellent communication skills; excellent writing skills.
• Excellent analytical thinking, practical problem-solving skills, and ability to work well with people at all levels.

Shift:

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