Responsibilities:
- Lead the development of ALLL, Loss Forecasting, Stress Testing, Capital Planning and CECL models using SAS/Python or R in collaboration with the on-shore team
- Perform in depth analysis on large data sets, and prepare analysis and reports to support discussions on key analytics and model risks
- Support building and enhancing procedures and model documentation in compliance with regulatory guidance as well as the Bank's model risk policy
- Support implementation and monitoring of ALLL and capital stress testing models
- Develop alternative predictive methodologies / tools to better identify credit dynamics in portfolio performance
- Proactively manage strong working relationships to maintain on-shore stakeholder satisfaction
- Assist in analyzing the current and future model landscape, technologies, data frameworks and implementation platforms in line with internal as well as industry best practices
- Develop and execute initiatives such as conducting applicable research and implementing industry best practices in modeling methodologies and management of model risk
- Maintain current/develop new analytical reports and presentations for senior management, executive committees and regulatory exams
- Perform other duties and/or special projects as assigned
Technical Skills
- Credit card modeling/analytics experience
- Proven hands-on experience utilizing SAS or SQL data mining skills as well as open-source tools such as R and Python.
- Advanced analytics using Excel/VBA, strong PowerPoint and documentation skills.
Desired Qualifications :
Experience/Knowledge
- Problem solving skills: Strong ability to rapidly learn the intricacies of an unfamiliar process, structure and scope complex problems, apply a range of analytical tools, gain and synthesize insights, and develop actionable recommendations
- Experience building/reviewing champion/challenger credit and risk models for consumer loss forecasting
- Comfort with Data and Technology: Prior experience functioning in roles and functions that are highly data-driven and require understanding of data models, process flows, and technology architecture as related to business requirements, including comfort interacting with internal technology teams
- Knowledge of external environment, industry/competitor profiles, and common macro-economic indicators and correlations
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