Quantitative Modeling Analyst I

BOK Financial•Tulsa, OK

23d

About The Position

The Quantitative Modeling Analyst I is responsible for identifying and managing risks associated with complex and strategically important models, including models used in AML, DFAST, CECL, Market Risk Rule, pricing, profitability, regulatory capital, underwriting, fraud, and fair lending. The role involves understanding model frameworks, assumptions, limitations, business decision-making context, analytical methods, and interactions between models used in the same process. The analyst also develops challenger models, assesses the impact of model limitations and errors, communicates findings, and supports remediation planning.

Requirements

Bachelor’s degree in Finance, Economics, or a STEM field.
At least 1 year of quantitative analysis experience in a discipline relevant to risk management, including statistical or econometric analysis, modeling, or mathematics.
At least 1 year of experience with analytics software such as SAS, R, SPSS, or Matlab and relational databases such as SQL.
Solid understanding of model performance measures.
Solid understanding of model development and validation methodologies and processes.
Strong project management skills.
Ability to produce high-quality technical documentation.
Knowledge of model risk management regulatory requirements, including FRB/OCC SR 11-7, Basel II/III capital requirements, and Dodd-Frank Act Stress Testing (DFAST).
Self-motivation, discipline, task focus, and a proven record of delivering high-quality results within strict deadlines.
Intellectual curiosity, strong problem-solving abilities, attention to detail, and prompt follow-through.
Ability to aggregate and synthesize complex data.
Outstanding oral and written communication skills.
Ability to present complex issues clearly and concisely.

Nice To Haves

Master’s degree in Finance, Economics, or a STEM field.
CFA, FRM, or CPA designation.

Responsibilities

Develop challenger models when validating vendor-designed models or when in-house models require more advanced analysis.
Measure the potential impact of model limitations, parameter estimation errors, and deviations from model assumptions.
Prepare clear written and verbal communications about model analyses, outcome discrepancies, identified risks, and regulatory or policy issues.
Assist with presentations to senior leaders, model owners, developers, and users.
Recommend management action plans and track remediation progress.