Sr. Quantitative Analyst (Model Validation) - PWB Westlake Village,CA

Pacific Western Bank - Westlake Village, CA (30+ days ago)3.9


The Sr. Quantitative Analyst, Model Validation responsible for assisting the Bank’s Model Risk Management Department with its framework and standards, model validation and model governance

RESPONSIBILITIES:
Conduct annual model reviews including process verification, performance monitoring, degree of changes, etc. and document the findings and comments in the annual review report.
Review on-going model monitoring reports, identify potential model risks; document the findings.

Conduct model validations on the Bank’s models, both in-house and vendor models, based on regulatory guidance, the Bank’s Model Risk Management policy and procedures, and the industry’s leading practices.

Evaluate model assumptions and weaknesses, data relevancy and completeness, conceptual soundness, modeling methodology, outcome analysis, etc.

Prepare model validation reports that meet the Model Risk Management Department’s expectations based on the evaluation of the models, including backtesting, sensitivity testing, benchmarking, etc. and present a conclusion based on the review.

Assist in model validation findings monitoring, reporting, and evaluating the corrective actions.

Support senior managers to communicate findings and recommendations from model validation reports to model owners and model users.

Work closely with model owners and model users to understand the model use and business applications.

Present findings to management as appropriate.

Assist in building benchmark models used in model validation team

POSITION REQUIREMENTS:
Bachelors or Master’s degree in Economics, Finance, Mathematics or other related quantitative fields.

3-5 years experience in model validation or model development in the banking industry or consulting.

Familiarity with model documentation requirements that meet regulatory expectations.

Strong written and verbal communication skills.

Familiarity with DFAST stress testing models is preferred.

Proficiency in statistical programming skills such as in R, SAS, etc.

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