Sr. Model Risk Analyst
The Model Risk Analyst will conduct independent model validation studies to manage and mitigate the risks that arise from the use of models that have fundamental errors and from the inappropriate use of model results. Analysis will include assessments of each model's conceptual soundness, statistical analysis of empirical model performance, the development of challenger models for benchmarking comparisons, and composition of robust validation reports. This role will identify items posing significant model risks to the bank, propose plans to address those risks, and present recommendations for continued model use or rejection to Senior Management and the Model Risk Committee.
- Perform reviews of existing model documentation. Interview model developers and model owners to understand the business context for model use and to facilitate the adoption of model risk management standards.
- Design specific validation plans to provide "effective challenge" to models, including assessments of overall design, underlying theoretical approaches, data quality and controls, model specification and estimation, development testing, implementation, use, and approvals.
- Analyze empirical model performance using statistical techniques.
- Review computer code and input data to assess quality.
- Prepare robust reports documenting the findings of validation review and analysis; present results.
- Assist model developers, model users, and model owners in the completion of model documentation and the design of scorecards to track the on-going performance of models under their responsibility.
- Facilitate improved understanding of model risk by conducting individual educational presentations or roadshow sessions as assigned by the Model Risk Manager.
- Lead third-party consultant validation or co-sourced validation projects as assigned.
- Design, implement and document standardized and automated model governance related workflows into Model Risk GRC platform.
- Oversee quality control of internal and consultant-produced model documentation, model validation reports, annual model reviews, and ongoing monitoring, as assigned.
- Monitor status of model remediation items and facilitate the resolution or escalation of issues in a timely fashion.
- Master's degree in economics, mathematics, statistics, financial engineering, quantitative finance, or actuarial sciences. Doctoral degree preferred.
- Certification as Financial Risk Manager, Professional Risk Manager, Chartered Financial Analyst, or Certificate in Quantitative Finance preferred.
- 5-8 years in banking or financial services as a Data Scientist, Statistician, Quantitative Risk Analyst, Model Developer, Model Validator, or similar.
- Previous experience as a team or project lead preferred.
- Demonstrated understanding of statistical modeling, econometric forecasting, machine learning, data extraction and processing techniques; and demonstrated ability to apply such methods in areas such as Credit Risk, Market Risk, Operational Risk, Asset & Liability Management, Stress Testing, or Economic Capital calculation.
- Knowledge of regulatory requirements related to model risk management, Basel II/III capital requirements, and Dodd-Frank Act Stress Testing.
- Advanced understanding of statistical modeling, econometric forecasting, machine learning, data extraction and processing techniques; and demonstrated ability to apply such methods.
- Experience with analytics software (SAS, R, SPSS, Matlab, Excel VBA, SQL), relational databases and/or 'Big Data' technologies.
- Possess communication skills, both oral and written, with ability to translate complex statistical or economic theories and analysis into practical implications for business teams and Senior Management.
- Ability to coach and mentor junior analysts in developing technical, communication and presentation skills.
- Demonstrate strong organizational skills, with the ability to manage multiple concurrent projects.
- Ability to proactively learn newly emerging statistical, econometric, and mathematical modeling techniques, and understand the implications of their use in a banking organization.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, citizenship, disability or protected veteran status.